R Notebook
library(Seurat)The legacy packages maptools, rgdal, and rgeos, underpinning this package
will retire shortly. Please refer to R-spatial evolution reports on
https://r-spatial.org/r/2023/05/15/evolution4.html for details.
This package is now running under evolution status 0
Registered S3 method overwritten by 'data.table':
method from
print.data.table
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
Attaching SeuratObject
Warning message:
R graphics engine version 15 is not supported by this version of RStudio. The Plots tab will be disabled until a newer version of RStudio is installed. library(tidyverse)Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql ── Attaching packages ───────────────────────── tidyverse 1.3.2 ──
✔ ggplot2 3.4.2 ✔ purrr 1.0.1
✔ tibble 3.2.1 ✔ dplyr 1.1.2
✔ tidyr 1.3.0 ✔ stringr 1.5.0
✔ readr 2.1.3 ✔ forcats 0.5.2
── Conflicts ──────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()#library(CelltypeR)samples
[1] "NPC3383" "NPC3383-iso" "NPC3575" "NPC3575-iso"
[5] "NPC3595" "NPC3595-iso" "NPC3940" "NPC3940N-iso"
[9] "NPC2965" "NPC3567" "NPC3567-iso" "NPCTD06"
[13] "NPCTD07" "NPC3448" "NPCTD06-iso" "NPC3123"
[17] "NPCQ65Q" "NPCAIW002-02" "NPC3940R-iso" "Final3123"
[21] "FinalP65P" "FinalQ65Q" "NPCP65P" "Final3383-iso"
[25] "Final3575-iso" "Final3595" "Final3595-iso" "Final3940"
[29] "Final3940N-iso" "Final3567" "NPCAIW001-02" "NPC2AIW002-02"
[33] "NPCTD22" "FinalAIW001-02" "FinalAIW002-02" "FinalTD07"
[37] "M1" "M2" "M3" "M4"
[41] "M5" "M6" "M7" "M8"
[45] "POOL_1NPC" "POOL_2NPC" "POOL_3NPC" "POOL_4Final" Add meta data information
DimPlot(seu)
# check with sample names
table(seu$sample, seu$CultureType)
NPC Neurons2weeks MotorNeurons
Final3123 0 796 0
Final3383-iso 0 1008 0
Final3567 0 940 0
Final3575-iso 0 1221 0
Final3595 0 1302 0
Final3595-iso 0 1546 0
Final3940 0 1643 0
Final3940N-iso 0 1238 0
FinalAIW001-02 0 1712 0
FinalAIW002-02 0 1439 0
FinalP65P 0 1709 0
FinalQ65Q 0 2739 0
FinalTD07 0 1032 0
M1 0 0 1290
M2 0 0 1627
M3 0 0 2463
M4 0 0 1883
M5 0 0 8202
M6 0 0 1160
M7 0 0 1968
M8 0 0 1215
NPC2965 279 0 0
NPC2AIW002-02 1478 0 0
NPC3123 512 0 0
NPC3383 800 0 0
NPC3383-iso 1030 0 0
NPC3448 1922 0 0
NPC3567 920 0 0
NPC3567-iso 1372 0 0
NPC3575 155 0 0
NPC3575-iso 1998 0 0
NPC3595 798 0 0
NPC3595-iso 1300 0 0
NPC3940 414 0 0
NPC3940N-iso 846 0 0
NPC3940R-iso 1105 0 0
NPCAIW001-02 1252 0 0
NPCAIW002-02 1840 0 0
NPCP65P 1146 0 0
NPCQ65Q 862 0 0
NPCTD06 1432 0 0
NPCTD06-iso 591 0 0
NPCTD07 1083 0 0
NPCTD22 1507 0 0
POOL_1NPC 876 0 0
POOL_2NPC 204 0 0
POOL_3NPC 1218 0 0
POOL_4Final 0 977 0Add the lines and then the diseases status
seu <- annotate(seu, annotations = new_line_vector, to_label = "sample",annotation_name = "Line")
DimPlot(seu)
# check
table(seu$sample, seu$Line)
3383 3383-iso 3575 3575-iso 3595 3595-iso 3940 3940N-iso
Final3123 0 0 0 0 0 0 0 0
Final3383-iso 0 1008 0 0 0 0 0 0
Final3567 0 0 0 0 0 0 0 0
Final3575-iso 0 0 0 1221 0 0 0 0
Final3595 0 0 0 0 1302 0 0 0
Final3595-iso 0 0 0 0 0 1546 0 0
Final3940 0 0 0 0 0 0 1643 0
Final3940N-iso 0 0 0 0 0 0 0 1238
FinalAIW001-02 0 0 0 0 0 0 0 0
FinalAIW002-02 0 0 0 0 0 0 0 0
FinalP65P 0 0 0 0 0 0 0 0
FinalQ65Q 0 0 0 0 0 0 0 0
FinalTD07 0 0 0 0 0 0 0 0
M1 0 0 0 0 0 0 0 0
M2 0 0 0 0 0 0 0 0
M3 0 0 0 0 0 0 0 0
M4 0 0 0 0 0 0 0 0
M5 0 0 0 0 0 0 0 0
M6 0 0 0 0 0 0 0 0
M7 0 0 0 0 0 0 0 0
M8 0 0 0 0 0 0 0 0
NPC2965 0 0 0 0 0 0 0 0
NPC2AIW002-02 0 0 0 0 0 0 0 0
NPC3123 0 0 0 0 0 0 0 0
NPC3383 800 0 0 0 0 0 0 0
NPC3383-iso 0 1030 0 0 0 0 0 0
NPC3448 0 0 0 0 0 0 0 0
NPC3567 0 0 0 0 0 0 0 0
NPC3567-iso 0 0 0 0 0 0 0 0
2965 3567 3567-iso TD06 TD07 3448 TD06-iso 3123-iso
Final3123 0 0 0 0 0 0 0 796
Final3383-iso 0 0 0 0 0 0 0 0
Final3567 0 940 0 0 0 0 0 0
Final3575-iso 0 0 0 0 0 0 0 0
Final3595 0 0 0 0 0 0 0 0
Final3595-iso 0 0 0 0 0 0 0 0
Final3940 0 0 0 0 0 0 0 0
Final3940N-iso 0 0 0 0 0 0 0 0
FinalAIW001-02 0 0 0 0 0 0 0 0
FinalAIW002-02 0 0 0 0 0 0 0 0
FinalP65P 0 0 0 0 0 0 0 0
FinalQ65Q 0 0 0 0 0 0 0 0
FinalTD07 0 0 0 0 1032 0 0 0
M1 0 0 0 0 0 0 0 0
M2 0 0 0 0 0 0 0 0
M3 0 0 0 0 0 0 0 0
M4 0 0 0 0 0 0 0 0
M5 0 0 0 0 0 0 0 0
M6 0 0 0 0 0 0 0 0
M7 0 0 0 0 0 0 0 0
M8 0 0 0 0 0 0 0 0
NPC2965 279 0 0 0 0 0 0 0
NPC2AIW002-02 0 0 0 0 0 0 0 0
NPC3123 0 0 0 0 0 0 0 512
NPC3383 0 0 0 0 0 0 0 0
NPC3383-iso 0 0 0 0 0 0 0 0
NPC3448 0 0 0 0 0 1922 0 0
NPC3567 0 920 0 0 0 0 0 0
NPC3567-iso 0 0 1372 0 0 0 0 0
3123-isoQ65Q AIW002-02 3940R-iso 3123-isoP65P AIW001-02
Final3123 0 0 0 0 0
Final3383-iso 0 0 0 0 0
Final3567 0 0 0 0 0
Final3575-iso 0 0 0 0 0
Final3595 0 0 0 0 0
Final3595-iso 0 0 0 0 0
Final3940 0 0 0 0 0
Final3940N-iso 0 0 0 0 0
FinalAIW001-02 0 0 0 0 1712
FinalAIW002-02 0 1439 0 0 0
FinalP65P 0 0 0 1709 0
FinalQ65Q 2739 0 0 0 0
FinalTD07 0 0 0 0 0
M1 0 0 0 0 0
M2 0 0 0 0 0
M3 0 0 0 0 0
M4 0 0 0 0 0
M5 0 0 0 0 0
M6 0 0 0 0 0
M7 0 0 0 0 0
M8 0 0 0 0 0
NPC2965 0 0 0 0 0
NPC2AIW002-02 0 1478 0 0 0
NPC3123 0 0 0 0 0
NPC3383 0 0 0 0 0
NPC3383-iso 0 0 0 0 0
NPC3448 0 0 0 0 0
NPC3567 0 0 0 0 0
NPC3567-iso 0 0 0 0 0
TD22 M1 M2 M3 M4 M5 M6 M7 M8 POOL_1NPC
Final3123 0 0 0 0 0 0 0 0 0 0
Final3383-iso 0 0 0 0 0 0 0 0 0 0
Final3567 0 0 0 0 0 0 0 0 0 0
Final3575-iso 0 0 0 0 0 0 0 0 0 0
Final3595 0 0 0 0 0 0 0 0 0 0
Final3595-iso 0 0 0 0 0 0 0 0 0 0
Final3940 0 0 0 0 0 0 0 0 0 0
Final3940N-iso 0 0 0 0 0 0 0 0 0 0
FinalAIW001-02 0 0 0 0 0 0 0 0 0 0
FinalAIW002-02 0 0 0 0 0 0 0 0 0 0
FinalP65P 0 0 0 0 0 0 0 0 0 0
FinalQ65Q 0 0 0 0 0 0 0 0 0 0
FinalTD07 0 0 0 0 0 0 0 0 0 0
M1 0 1290 0 0 0 0 0 0 0 0
M2 0 0 1627 0 0 0 0 0 0 0
M3 0 0 0 2463 0 0 0 0 0 0
M4 0 0 0 0 1883 0 0 0 0 0
M5 0 0 0 0 0 8202 0 0 0 0
M6 0 0 0 0 0 0 1160 0 0 0
M7 0 0 0 0 0 0 0 1968 0 0
M8 0 0 0 0 0 0 0 0 1215 0
NPC2965 0 0 0 0 0 0 0 0 0 0
NPC2AIW002-02 0 0 0 0 0 0 0 0 0 0
NPC3123 0 0 0 0 0 0 0 0 0 0
NPC3383 0 0 0 0 0 0 0 0 0 0
NPC3383-iso 0 0 0 0 0 0 0 0 0 0
NPC3448 0 0 0 0 0 0 0 0 0 0
NPC3567 0 0 0 0 0 0 0 0 0 0
NPC3567-iso 0 0 0 0 0 0 0 0 0 0
POOL_2NPC POOL_3NPC POOL_4Final
Final3123 0 0 0
Final3383-iso 0 0 0
Final3567 0 0 0
Final3575-iso 0 0 0
Final3595 0 0 0
Final3595-iso 0 0 0
Final3940 0 0 0
Final3940N-iso 0 0 0
FinalAIW001-02 0 0 0
FinalAIW002-02 0 0 0
FinalP65P 0 0 0
FinalQ65Q 0 0 0
FinalTD07 0 0 0
M1 0 0 0
M2 0 0 0
M3 0 0 0
M4 0 0 0
M5 0 0 0
M6 0 0 0
M7 0 0 0
M8 0 0 0
NPC2965 0 0 0
NPC2AIW002-02 0 0 0
NPC3123 0 0 0
NPC3383 0 0 0
NPC3383-iso 0 0 0
NPC3448 0 0 0
NPC3567 0 0 0
NPC3567-iso 0 0 0
[ reached getOption("max.print") -- omitted 19 rows ]DimPlot(seu)
# add disease status
# Create the lookup table for Line to DiseaseStatus mapping
line_to_disease <- c("3448" = "HC",
"TD22" = "HC",
"AIW001-02" = "HC",
"AIW002-02" = "HC",
"2965" = "PD",
"3383" = "PD",
"3575" = "PD",
"TD06" = "PD",
"TD07" = "PD",
"3123" = "PD",
"3567" = "PD",
"3595" = "PD",
"3940" = "PD",
"3567-iso" = "PD-iso",
"3940R-iso" = "PD-iso",
"TD06-iso" = "PD-iso",
"3940N-iso" = "PD-iso",
"3123-isoQ65Q" = "PD-iso",
"3123-isoP65P" = "PD-iso",
"3123-iso" = "PD-iso",
"3383-iso" = "PD-iso",
"3575-iso" = "PD-iso",
"3595-iso" = "PD-iso")
# Assuming "Line" is your starting character vector
Idents(seu) <- "Line"
Line <- levels(seu)
# Create a new vector of disease statuses using the lookup table
disease_status_vector <- ifelse(Line %in% names(line_to_disease),
line_to_disease[Line],
"other")
# Print the new vector of disease statuses
print(disease_status_vector) [1] "PD" "PD-iso" "PD" "PD-iso" "PD" "PD-iso" "PD"
[8] "PD-iso" "PD" "PD" "PD-iso" "PD" "PD" "HC"
[15] "PD-iso" "PD-iso" "PD-iso" "HC" "PD-iso" "PD-iso" "HC"
[22] "HC" "other" "other" "other" "other" "other" "other"
[29] "other" "other" "other" "other" "other" "other" seu <- annotate(seu, annotations = disease_status_vector, to_label = "Line",annotation_name = "DiseaseStatus")
# check
table(seu$Line, seu$DiseaseStatus)
PD PD-iso HC other
3383 800 0 0 0
3383-iso 0 2038 0 0
3575 155 0 0 0
3575-iso 0 3219 0 0
3595 2100 0 0 0
3595-iso 0 2846 0 0
3940 2057 0 0 0
3940N-iso 0 2084 0 0
2965 279 0 0 0
3567 1860 0 0 0
3567-iso 0 1372 0 0
TD06 1432 0 0 0
TD07 2115 0 0 0
3448 0 0 1922 0
TD06-iso 0 591 0 0
3123-iso 0 1308 0 0
3123-isoQ65Q 0 3601 0 0
AIW002-02 0 0 4757 0
3940R-iso 0 1105 0 0
3123-isoP65P 0 2855 0 0
AIW001-02 0 0 2964 0
TD22 0 0 1507 0
M1 0 0 0 1290
M2 0 0 0 1627
M3 0 0 0 2463
M4 0 0 0 1883
M5 0 0 0 8202
M6 0 0 0 1160
M7 0 0 0 1968
M8 0 0 0 1215
POOL_1NPC 0 0 0 876
POOL_2NPC 0 0 0 204
POOL_3NPC 0 0 0 1218
POOL_4Final 0 0 0 977table(sample)Error in unique.default(x, nmax = nmax) :
unique() applies only to vectorscellcounts <- as.data.frame(table(seu$Line,seu$CultureType))
table(seu$Line,seu$CultureType,seu$DiseaseStatus), , = PD
NPC Neurons2weeks MotorNeurons
3383 800 0 0
3383-iso 0 0 0
3575 155 0 0
3575-iso 0 0 0
3595 798 1302 0
3595-iso 0 0 0
3940 414 1643 0
3940N-iso 0 0 0
2965 279 0 0
3567 920 940 0
3567-iso 0 0 0
TD06 1432 0 0
TD07 1083 1032 0
3448 0 0 0
TD06-iso 0 0 0
3123-iso 0 0 0
3123-isoQ65Q 0 0 0
AIW002-02 0 0 0
3940R-iso 0 0 0
3123-isoP65P 0 0 0
AIW001-02 0 0 0
TD22 0 0 0
M1 0 0 0
M2 0 0 0
M3 0 0 0
M4 0 0 0
M5 0 0 0
M6 0 0 0
M7 0 0 0
M8 0 0 0
POOL_1NPC 0 0 0
POOL_2NPC 0 0 0
POOL_3NPC 0 0 0
POOL_4Final 0 0 0
, , = PD-iso
NPC Neurons2weeks MotorNeurons
3383 0 0 0
3383-iso 1030 1008 0
3575 0 0 0
3575-iso 1998 1221 0
3595 0 0 0
3595-iso 1300 1546 0
3940 0 0 0
3940N-iso 846 1238 0
2965 0 0 0
3567 0 0 0
3567-iso 1372 0 0
TD06 0 0 0
TD07 0 0 0
3448 0 0 0
TD06-iso 591 0 0
3123-iso 512 796 0
3123-isoQ65Q 862 2739 0
AIW002-02 0 0 0
3940R-iso 1105 0 0
3123-isoP65P 1146 1709 0
AIW001-02 0 0 0
TD22 0 0 0
M1 0 0 0
M2 0 0 0
M3 0 0 0
M4 0 0 0
M5 0 0 0
M6 0 0 0
M7 0 0 0
M8 0 0 0
POOL_1NPC 0 0 0
POOL_2NPC 0 0 0
POOL_3NPC 0 0 0
POOL_4Final 0 0 0
, , = HC
NPC Neurons2weeks MotorNeurons
3383 0 0 0
3383-iso 0 0 0
3575 0 0 0
3575-iso 0 0 0
3595 0 0 0
3595-iso 0 0 0
3940 0 0 0
3940N-iso 0 0 0
2965 0 0 0
3567 0 0 0
3567-iso 0 0 0
TD06 0 0 0
TD07 0 0 0
3448 1922 0 0
TD06-iso 0 0 0
3123-iso 0 0 0
3123-isoQ65Q 0 0 0
AIW002-02 3318 1439 0
3940R-iso 0 0 0
3123-isoP65P 0 0 0
AIW001-02 1252 1712 0
TD22 1507 0 0
M1 0 0 0
M2 0 0 0
M3 0 0 0
M4 0 0 0
M5 0 0 0
M6 0 0 0
M7 0 0 0
M8 0 0 0
POOL_1NPC 0 0 0
POOL_2NPC 0 0 0
POOL_3NPC 0 0 0
POOL_4Final 0 0 0
, , = other
NPC Neurons2weeks MotorNeurons
3383 0 0 0
3383-iso 0 0 0
3575 0 0 0
3575-iso 0 0 0
3595 0 0 0
3595-iso 0 0 0
3940 0 0 0
3940N-iso 0 0 0
2965 0 0 0
3567 0 0 0
3567-iso 0 0 0
TD06 0 0 0
TD07 0 0 0
3448 0 0 0
TD06-iso 0 0 0
3123-iso 0 0 0
3123-isoQ65Q 0 0 0
AIW002-02 0 0 0
3940R-iso 0 0 0
3123-isoP65P 0 0 0
AIW001-02 0 0 0
TD22 0 0 0
M1 0 0 1290
M2 0 0 1627
M3 0 0 2463
M4 0 0 1883
M5 0 0 8202
M6 0 0 1160
M7 0 0 1968
M8 0 0 1215
POOL_1NPC 876 0 0
POOL_2NPC 204 0 0
POOL_3NPC 1218 0 0
POOL_4Final 0 977 0# Convert long_df to the wide format
wide_df <- pivot_wider(cellcounts,
id_cols = Var1,
names_from = Var2,
values_from = Freq)
write.csv(wide_df,"cellcounts_sample.csv")
DimPlot(seu, group.by = "DiseaseStatus")
Subset for to remove pools and Maria’s samples
Reprocess subset
seu.n <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamples.RDS")
Get clusters
Subset out neurons and NPC
#seu.n$CultureType
Idents(seu.n) <- "CultureType"
neurons <- subset(seu.n, idents = "Neurons2weeks")
DimPlot(seu)
DimPlot(seu, group.by = "CultureType")
DimPlot(seu, group.by = "Line")
Integration
save objects
saveRDS(seu, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/Parse48_allSamples.RDS")
saveRDS(seu.n, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamples.RDS")
table(NPC$DiseaseStatus,NPC$Line)
3383 3383-iso 3575 3575-iso 3595 3595-iso 3940 3940N-iso 2965 3567
PD 800 0 155 0 798 0 414 0 279 920
PD-iso 0 1030 0 1998 0 1300 0 846 0 0
HC 0 0 0 0 0 0 0 0 0 0
other 0 0 0 0 0 0 0 0 0 0
3567-iso TD06 TD07 3448 TD06-iso 3123-iso 3123-isoQ65Q AIW002-02
PD 0 1432 1083 0 0 0 0 0
PD-iso 1372 0 0 0 591 512 862 0
HC 0 0 0 1922 0 0 0 3318
other 0 0 0 0 0 0 0 0
3940R-iso 3123-isoP65P AIW001-02 TD22 M1 M2 M3 M4 M5 M6
PD 0 0 0 0 0 0 0 0 0 0
PD-iso 1105 1146 0 0 0 0 0 0 0 0
HC 0 0 1252 1507 0 0 0 0 0 0
other 0 0 0 0 0 0 0 0 0 0
M7 M8 POOL_1NPC POOL_2NPC POOL_3NPC POOL_4Final
PD 0 0 0 0 0 0
PD-iso 0 0 0 0 0 0
HC 0 0 0 0 0 0
other 0 0 0 0 0 0Now integrate samples
sublist <- SplitObject(NPC.PDiso, split.by = "Line")
# normalize and find variable features
for (i in 1:length(sublist)){
sublist[[i]] <- NormalizeData(sublist[[i]], verbose = FALSE)
sublist[[i]] <- FindVariableFeatures(sublist[[i]], selection.method = "vst")
}
Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
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Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
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Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
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Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
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Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
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[----|----|----|----|----|----|----|----|----|----|
**************************************************|# Create an empty Seurat object to store the integrated data
# Take the first Seurat object from the list as the starting point
integrated_seurat <- subset(sublist[[1]])
# Iterate over the list of Seurat objects
for (i in 1:length(sublist)) {
# Rename the 'orig.ident' metadata inside the seurat object to match the object name in the list
sublist[[i]]$orig.ident <- names(sublist)[i]
}
sample.list <- sublist
for (i in 1:length(sample.list)) {
# Normalize and scale the data
sample.list[[i]] <- NormalizeData(sample.list[[i]], verbose = FALSE)
sample.list[[i]] <- ScaleData(sample.list[[i]], verbose = FALSE)
# Find variable features
sample.list[[i]] <- FindVariableFeatures(sample.list[[i]], selection.method = "vst")
# Get the variable features
variable_features <- VariableFeatures(sample.list[[i]])
# Run PCA with the variable features
sample.list[[i]] <- RunPCA(sample.list[[i]], verbose = FALSE, npcs = 30, features = variable_features)
}Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|int.anchors <- FindIntegrationAnchors(object.list = sample.list, dims = 1:30, reduction = "rpca")Computing 2000 integration features
Scaling features for provided objects
| | 0 % ~calculating
|+++++ | 10% ~01s
|++++++++++ | 20% ~02s
|+++++++++++++++ | 30% ~01s
|++++++++++++++++++++ | 40% ~01s
|+++++++++++++++++++++++++ | 50% ~01s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~00s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s Computing within dataset neighborhoods
| | 0 % ~calculating
|+++++ | 10% ~02s
|++++++++++ | 20% ~03s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Finding all pairwise anchors
| | 0 % ~calculating Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 866 anchors
|++ | 2 % ~01m 33s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 511 anchors
|+++ | 4 % ~01m 24s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1437 anchors
|++++ | 7 % ~01m 27s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 547 anchors
|+++++ | 9 % ~01m 20s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1525 anchors
|++++++ | 11% ~01m 19s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1309 anchors
|+++++++ | 13% ~01m 15s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1008 anchors
|++++++++ | 16% ~01m 13s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 657 anchors
|+++++++++ | 18% ~01m 13s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 484 anchors
|++++++++++ | 20% ~01m 11s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 483 anchors
|++++++++++++ | 22% ~01m 08s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 825 anchors
|+++++++++++++ | 24% ~01m 05s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 569 anchors
|++++++++++++++ | 27% ~01m 03s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 400 anchors
|+++++++++++++++ | 29% ~01m 01s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 434 anchors
|++++++++++++++++ | 31% ~57s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 930 anchors
|+++++++++++++++++ | 33% ~55s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 538 anchors
|++++++++++++++++++ | 36% ~52s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1306 anchors
|+++++++++++++++++++ | 38% ~51s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 654 anchors
|++++++++++++++++++++ | 40% ~49s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 712 anchors
|++++++++++++++++++++++ | 42% ~46s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 489 anchors
|+++++++++++++++++++++++ | 44% ~44s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 434 anchors
|++++++++++++++++++++++++ | 47% ~42s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 561 anchors
|+++++++++++++++++++++++++ | 49% ~40s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1752 anchors
|++++++++++++++++++++++++++ | 51% ~38s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 961 anchors
|+++++++++++++++++++++++++++ | 53% ~37s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1183 anchors
|++++++++++++++++++++++++++++ | 56% ~35s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 512 anchors
|+++++++++++++++++++++++++++++ | 58% ~33s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 449 anchors
|++++++++++++++++++++++++++++++ | 60% ~31s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 887 anchors
|++++++++++++++++++++++++++++++++ | 62% ~29s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 897 anchors
|+++++++++++++++++++++++++++++++++ | 64% ~27s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 689 anchors
|++++++++++++++++++++++++++++++++++ | 67% ~26s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 461 anchors
|+++++++++++++++++++++++++++++++++++ | 69% ~24s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 502 anchors
|++++++++++++++++++++++++++++++++++++ | 71% ~22s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1401 anchors
|+++++++++++++++++++++++++++++++++++++ | 73% ~21s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 868 anchors
|++++++++++++++++++++++++++++++++++++++ | 76% ~19s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 470 anchors
|+++++++++++++++++++++++++++++++++++++++ | 78% ~17s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 527 anchors
|++++++++++++++++++++++++++++++++++++++++ | 80% ~15s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 753 anchors
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~14s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 2282 anchors
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~12s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1102 anchors
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~10s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1239 anchors
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~09s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 682 anchors
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 558 anchors
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1248 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~03s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1479 anchors
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 680 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 18sintegrated_seurat <- IntegrateData(anchorset = int.anchors, dims = 1:30)Merging dataset 7 into 2
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 10 into 2 7
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 8 into 2 7 10
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 6 into 5
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 4 into 3
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 9 into 5 6
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 3 4 into 2 7 10 8
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 1 into 5 6 9
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 5 6 9 1 into 2 7 10 8 3 4
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating dataint.PDiso <- integrated_seurat
Idents(int.PDiso) <- "Line"
levels(int.PDiso) [1] "3383-iso" "3575-iso" "3595-iso" "3940N-iso"
[5] "3567-iso" "TD06-iso" "3123-iso" "3123-isoQ65Q"
[9] "3940R-iso" "3123-isoP65P"# an integrate funcrion
# Define a function for integrating a list of Seurat objects
integrate_seurat_objects <- function(seurat_list, dims = 1:30) {
# Normalize and find variable features for each object
for (i in 1:length(seurat_list)) {
seurat_list[[i]] <- NormalizeData(seurat_list[[i]], verbose = FALSE)
seurat_list[[i]] <- FindVariableFeatures(seurat_list[[i]], selection.method = "vst")
}
# Create an empty Seurat object to store the integrated data
integrated_seurat <- subset(seurat_list[[1]])
# Iterate over the list of Seurat objects
for (i in 1:length(seurat_list)) {
# Rename the 'orig.ident' metadata inside the Seurat object to match the object name in the list
seurat_list[[i]]$orig.ident <- names(seurat_list)[i]
}
sample.list <- seurat_list
for (i in 1:length(sample.list)) {
# Normalize and scale the data
sample.list[[i]] <- NormalizeData(sample.list[[i]], verbose = FALSE)
sample.list[[i]] <- ScaleData(sample.list[[i]], verbose = FALSE)
# Find variable features
sample.list[[i]] <- FindVariableFeatures(sample.list[[i]], selection.method = "vst")
# Get the variable features
variable_features <- VariableFeatures(sample.list[[i]])
# Run PCA with the variable features
sample.list[[i]] <- RunPCA(sample.list[[i]], verbose = FALSE, npcs = 30, features = variable_features)
}
# Find integration anchors
int.anchors <- FindIntegrationAnchors(object.list = sample.list, dims = dims, reduction = "rpca")
# Integrate data
integrated_seurat <- IntegrateData(anchorset = int.anchors, dims = dims)
return(integrated_seurat)
}
Use function to integrate the HC
sublist <- SplitObject(NPC.PDiso, split.by = "Line")
int.iso <- integrate_seurat_objects(sublist, dims = 1:30)
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Computing 2000 integration features
Scaling features for provided objects
| | 0 % ~calculating
|+++++ | 10% ~02s
|++++++++++ | 20% ~02s
|+++++++++++++++ | 30% ~02s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Computing within dataset neighborhoods
| | 0 % ~calculating
|+++++ | 10% ~02s
|++++++++++ | 20% ~03s
|+++++++++++++++ | 30% ~03s
|++++++++++++++++++++ | 40% ~02s
|+++++++++++++++++++++++++ | 50% ~02s
|++++++++++++++++++++++++++++++ | 60% ~01s
|+++++++++++++++++++++++++++++++++++ | 70% ~01s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~01s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~00s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Finding all pairwise anchors
| | 0 % ~calculating Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 866 anchors
|++ | 2 % ~01m 44s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 511 anchors
|+++ | 4 % ~01m 43s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1437 anchors
|++++ | 7 % ~01m 42s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 547 anchors
|+++++ | 9 % ~01m 32s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1525 anchors
|++++++ | 11% ~01m 31s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1309 anchors
|+++++++ | 13% ~01m 26s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1008 anchors
|++++++++ | 16% ~01m 25s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 657 anchors
|+++++++++ | 18% ~01m 24s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 484 anchors
|++++++++++ | 20% ~01m 21s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 483 anchors
|++++++++++++ | 22% ~01m 18s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 825 anchors
|+++++++++++++ | 24% ~01m 14s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 569 anchors
|++++++++++++++ | 27% ~01m 11s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 400 anchors
|+++++++++++++++ | 29% ~01m 09s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 434 anchors
|++++++++++++++++ | 31% ~01m 05s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 930 anchors
|+++++++++++++++++ | 33% ~01m 02s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 538 anchors
|++++++++++++++++++ | 36% ~59s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1306 anchors
|+++++++++++++++++++ | 38% ~58s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 654 anchors
|++++++++++++++++++++ | 40% ~55s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 712 anchors
|++++++++++++++++++++++ | 42% ~52s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 489 anchors
|+++++++++++++++++++++++ | 44% ~50s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 434 anchors
|++++++++++++++++++++++++ | 47% ~47s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 561 anchors
|+++++++++++++++++++++++++ | 49% ~45s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1752 anchors
|++++++++++++++++++++++++++ | 51% ~44s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 961 anchors
|+++++++++++++++++++++++++++ | 53% ~42s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1183 anchors
|++++++++++++++++++++++++++++ | 56% ~39s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 512 anchors
|+++++++++++++++++++++++++++++ | 58% ~37s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 449 anchors
|++++++++++++++++++++++++++++++ | 60% ~35s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 887 anchors
|++++++++++++++++++++++++++++++++ | 62% ~33s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 897 anchors
|+++++++++++++++++++++++++++++++++ | 64% ~31s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 689 anchors
|++++++++++++++++++++++++++++++++++ | 67% ~29s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 461 anchors
|+++++++++++++++++++++++++++++++++++ | 69% ~28s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 502 anchors
|++++++++++++++++++++++++++++++++++++ | 71% ~25s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1401 anchors
|+++++++++++++++++++++++++++++++++++++ | 73% ~24s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 868 anchors
|++++++++++++++++++++++++++++++++++++++ | 76% ~21s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 470 anchors
|+++++++++++++++++++++++++++++++++++++++ | 78% ~19s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 527 anchors
|++++++++++++++++++++++++++++++++++++++++ | 80% ~17s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 753 anchors
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~16s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 2282 anchors
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~14s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1102 anchors
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~12s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1239 anchors
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~10s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 682 anchors
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~08s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 558 anchors
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~06s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1248 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 1479 anchors
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 680 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 27sError: vector memory exhausted (limit reached?)
Error during wrapup: vector memory exhausted (limit reached?)
Error: no more error handlers available (recursive errors?); invoking 'abort' restartNPC_list <- list("HC" = int.HC,"PD" = int.PD,"PD-iso" = int.PDiso)
#listPDHC <- list("HC" = int.HC, "PD" = int.PD)
# Integrate the Seurat objects
integrated_seurat <- integrate_seurat_objects(NPC_list, dims = 1:20)
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Computing 2000 integration features
Scaling features for provided objects
| | 0 % ~calculating
|+++++++++++++++++ | 33% ~01s
|++++++++++++++++++++++++++++++++++ | 67% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=03s Computing within dataset neighborhoods
| | 0 % ~calculating
|+++++++++++++++++ | 33% ~05s
|++++++++++++++++++++++++++++++++++ | 67% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=07s Finding all pairwise anchors
| | 0 % ~calculating Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 3405 anchors
|+++++++++++++++++ | 33% ~17s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 3580 anchors
|++++++++++++++++++++++++++++++++++ | 67% ~10s Projecting new data onto SVD
Projecting new data onto SVD
Finding neighborhoods
Finding anchors
Found 5952 anchors
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=30s Merging dataset 2 into 3
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
Merging dataset 1 into 3 2
Extracting anchors for merged samples
Finding integration vectors
Finding integration vector weights
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Integrating data
int.PD <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNPCintPD.RDS")
int. HC <-readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNPCintHC.RDS")
Error: unexpected symbol in "int. HC"seu <- RunUMAP(seu, reduction = "pca", dims = 1:20, n.neighbors = 81)
Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per session
08:00:45 UMAP embedding parameters a = 0.9922 b = 1.112
08:00:45 Read 24642 rows and found 20 numeric columns
08:00:45 Using Annoy for neighbor search, n_neighbors = 81
08:00:45 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:00:47 Writing NN index file to temp file /var/folders/k4/khtkczkd5tn732ftjpwgtr240000gn/T//RtmpnhJIKK/fileca8a75076310
08:00:48 Searching Annoy index using 1 thread, search_k = 8100
08:01:07 Annoy recall = 100%
08:01:07 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 81
08:01:11 Initializing from normalized Laplacian + noise (using irlba)
08:01:13 Commencing optimization for 200 epochs, with 2439236 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
08:01:47 Optimization finishedseu <- FindVariableFeatures(seu)Warning in FindVariableFeatures.Assay(object = assay.data, selection.method = selection.method, :
selection.method set to 'vst' but count slot is empty; will use data slot instead
Calculating gene variances
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Warning in eval(predvars, data, env) : NaNs produced
Warning in hvf.info$variance.expected[not.const] <- 10^fit$fitted :
number of items to replace is not a multiple of replacement length
Calculating feature variances of standardized and clipped values
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|seu <- FindNeighbors(seu, dims = 1:20, k.param = 81)Computing nearest neighbor graph
Computing SNNseu <- FindClusters(seu, resolution = 0.3)Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
Number of nodes: 24642
Number of edges: 3081773
Running Louvain algorithm...0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|Maximum modularity in 10 random starts: 0.9296
Number of communities: 12
Elapsed time: 13 secondsDimPlot(seu)
DimPlot(seu, group.by = "Line")
DimPlot(seu, group.by = "DiseaseStatus")
saveRDS(seu,"/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesintegratedNPCs.RDS")
Annotate the NPC clusters
- Find markers and Check Enricher
- Get marker list score and
Marker list
da_neurons <- c("TH","SLC6A3","SLC18A2","SOX6","NDNF","SNCG","ALDH1A1","CALB1","TACR2","SLC17A6","SLC32A1","OTX2","GRP","LPL","CCK","VIP")
NPC_orStemLike <- c("DCX","NEUROD1","TBR1","PCNA","MKI67","SOX2","NES","PAX6","MASH1")
mature_neurons = c("RBFOX3","SYP","DLG45","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6")
excitatory_neurons = c("GRIA2","GRIA1","GRIA4","GRIN1","GRIN2B","GRIN2A","GRIN3A","GRIN3","GRIP1","CAMK2A")
inhbitory_neurons = c("GAD1","GAD2", "GAT1","PVALB","GABR2","GABR1","GBRR1","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")
astrocytes <- c("GFAP","S100B","AQP4","APOE", "SOX9","SLC1A3")
oligodendrocytes <- c("MBP","MOG","OLIG1","OLIG2","SOX10")
radial_glia <- c("PTPRC","AIF1","ADGRE1", "VIM", "TNC","PTPRZ1","FAM107A","HOPX","LIFR",
"ITGB5","IL6ST","SLC1A3")
epithelial <- c("HES1","HES5","SOX2","SOX10","NES","CDH1","NOTCH1")
microglia <- c("IBA1","P2RY12","P2RY13","TREM119", "GPR34","SIGLECH","TREM2",
"CX3CR1","FCRLS","OLFML3","HEXB","TGFBR1", "SALL1","MERTK",
"PROS1")
features_list <- c("MKI67","SOX2","POU5F1","DLX2","PAX6","SOX9","HES1","NES","RBFOX3","MAP2","NCAM1","CD24","GRIA2","GRIN2B","GABBR1","GAD1","GAD2","GABRA1","GABRB2","TH","ALDH1A1","LMX1B","NR4A2","CORIN","CALB1","KCNJ6","CXCR4","ITGA6","SLC1A3","CD44","AQP4","S100B", "PDGFRA","OLIG2","MBP","CLDN11","VIM","VCAM1")
short_list <- c("MKI67","SOX9","HES1","NES","DLX2","RBFOX3","MAP2","TH","CALB1","KCNJ6","SLC1A3","CD44","AQP4","S100B","OLIG2","MBP","VIM")
gene_lists = list("DA_neurons" = da_neurons, "NPC" = NPC_orStemLike,
"Neurons" = mature_neurons, "ExNeurons" = excitatory_neurons,
"InhNeurons" = inhbitory_neurons, "Astrocytes" = astrocytes,
"Oligo" = oligodendrocytes, "RadialGlia" = radial_glia,
"Epithelial" = epithelial, "Microglia" = microglia )
module_scores_per_gene_list <- calculate_module_scores(seu, gene_lists)
Error in base::rowMeans(x, na.rm = na.rm, dims = dims, ...) :
'x' must be numericIdents(seu) <- "integrated_snn_res.0.3"
for (i in da_neurons) {
print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
Idents(seu) <- "integrated_snn_res.0.3"
for (i in NPC_orStemLike) {
print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}Warning in FetchData.Seurat(object = object, vars = c(dims, "ident", features), :
The following requested variables were not found: MASH1
Error: None of the requested features were found: MASH1 in slot data
Idents(seu) <- "integrated_snn_res.0.3"
for (i in mature_neurons) {
print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
DimPlot(seu)
Idents(seu) <- "integrated_snn_res.0.3"
for (i in astrocytes) {
print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
Idents(seu) <- "integrated_snn_res.0.3"
for (i in radial_glia) {
print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
Idents(seu) <- "integrated_snn_res.0.3"
for (i in epithelial) {
print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
Idents(seu) <- "integrated_snn_res.0.3"
for (i in oligodendrocytes) {
print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
Find Markers
ClusterMarkers <- FindAllMarkers(seu, only.pos = TRUE)Calculating cluster 0
| | 0 % ~calculating
|+ | 1 % ~01m 33s
|++ | 2 % ~01m 25s
|++ | 3 % ~01m 20s
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|++++++++ | 15% ~01m 06s
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|++++++++++++ | 22% ~60s
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|++++++++++++++++ | 30% ~54s
|++++++++++++++++ | 31% ~53s
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|++++++++++++++++++++ | 38% ~48s
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|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 15sCalculating cluster 1
| | 0 % ~calculating
|+ | 1 % ~01m 42s
|++ | 2 % ~01m 34s
|++ | 3 % ~01m 32s
|+++ | 4 % ~01m 30s
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|+++++++++++++++++ | 33% ~01m 02s
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|++++++++++++++++++ | 35% ~60s
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|++++++++++++++++++++++++ | 47% ~49s
|++++++++++++++++++++++++ | 48% ~48s
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|+++++++++++++++++++++++++++ | 53% ~44s
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|++++++++++++++++++++++++++++++++ | 63% ~34s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 31sCalculating cluster 2
| | 0 % ~calculating
|+ | 1 % ~01m 59s
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|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~17s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~16s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~14s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~13s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~12s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 59sCalculating cluster 3
| | 0 % ~calculating
|+ | 1 % ~01m 29s
|++ | 2 % ~01m 32s
|++ | 3 % ~01m 34s
|+++ | 4 % ~01m 32s
|+++ | 5 % ~01m 33s
|++++ | 6 % ~01m 32s
|++++ | 7 % ~01m 31s
|+++++ | 8 % ~01m 30s
|+++++ | 9 % ~01m 28s
|++++++ | 11% ~01m 28s
|++++++ | 12% ~01m 27s
|+++++++ | 13% ~01m 25s
|+++++++ | 14% ~01m 24s
|++++++++ | 15% ~01m 23s
|++++++++ | 16% ~01m 22s
|+++++++++ | 17% ~01m 21s
|+++++++++ | 18% ~01m 20s
|++++++++++ | 19% ~01m 19s
|++++++++++ | 20% ~01m 17s
|+++++++++++ | 21% ~01m 16s
|++++++++++++ | 22% ~01m 15s
|++++++++++++ | 23% ~01m 14s
|+++++++++++++ | 24% ~01m 13s
|+++++++++++++ | 25% ~01m 11s
|++++++++++++++ | 26% ~01m 10s
|++++++++++++++ | 27% ~01m 10s
|+++++++++++++++ | 28% ~01m 08s
|+++++++++++++++ | 29% ~01m 07s
|++++++++++++++++ | 31% ~01m 06s
|++++++++++++++++ | 32% ~01m 05s
|+++++++++++++++++ | 33% ~01m 04s
|+++++++++++++++++ | 34% ~01m 03s
|++++++++++++++++++ | 35% ~01m 02s
|++++++++++++++++++ | 36% ~01m 01s
|+++++++++++++++++++ | 37% ~60s
|+++++++++++++++++++ | 38% ~59s
|++++++++++++++++++++ | 39% ~58s
|++++++++++++++++++++ | 40% ~57s
|+++++++++++++++++++++ | 41% ~57s
|++++++++++++++++++++++ | 42% ~56s
|++++++++++++++++++++++ | 43% ~55s
|+++++++++++++++++++++++ | 44% ~54s
|+++++++++++++++++++++++ | 45% ~53s
|++++++++++++++++++++++++ | 46% ~52s
|++++++++++++++++++++++++ | 47% ~51s
|+++++++++++++++++++++++++ | 48% ~49s
|+++++++++++++++++++++++++ | 49% ~48s
|++++++++++++++++++++++++++ | 51% ~47s
|++++++++++++++++++++++++++ | 52% ~47s
|+++++++++++++++++++++++++++ | 53% ~46s
|+++++++++++++++++++++++++++ | 54% ~45s
|++++++++++++++++++++++++++++ | 55% ~44s
|++++++++++++++++++++++++++++ | 56% ~43s
|+++++++++++++++++++++++++++++ | 57% ~42s
|+++++++++++++++++++++++++++++ | 58% ~41s
|++++++++++++++++++++++++++++++ | 59% ~40s
|++++++++++++++++++++++++++++++ | 60% ~02m 24s
|+++++++++++++++++++++++++++++++ | 61% ~02m 18s
|++++++++++++++++++++++++++++++++ | 62% ~02m 13s
|++++++++++++++++++++++++++++++++ | 63% ~02m 07s
|+++++++++++++++++++++++++++++++++ | 64% ~02m 02s
|+++++++++++++++++++++++++++++++++ | 65% ~01m 57s
|++++++++++++++++++++++++++++++++++ | 66% ~01m 52s
|++++++++++++++++++++++++++++++++++ | 67% ~01m 48s
|+++++++++++++++++++++++++++++++++++ | 68% ~01m 43s
|+++++++++++++++++++++++++++++++++++ | 69% ~01m 38s
|++++++++++++++++++++++++++++++++++++ | 71% ~01m 34s
|++++++++++++++++++++++++++++++++++++ | 72% ~01m 30s
|+++++++++++++++++++++++++++++++++++++ | 73% ~01m 26s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01m 21s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01m 17s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01m 13s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01m 10s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~01m 06s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01m 02s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~58s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~55s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~51s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~48s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~45s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~41s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~38s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~35s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~32s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~29s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~26s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~23s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~20s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~17s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~14s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=04m 12sCalculating cluster 4
| | 0 % ~calculating
|+ | 1 % ~01m 04s
|++ | 2 % ~01m 07s
|++ | 3 % ~01m 08s
|+++ | 5 % ~01m 08s
|+++ | 6 % ~01m 07s
|++++ | 7 % ~01m 06s
|+++++ | 8 % ~01m 05s
|+++++ | 9 % ~01m 04s
|++++++ | 10% ~01m 03s
|++++++ | 11% ~01m 02s
|+++++++ | 13% ~01m 01s
|+++++++ | 14% ~60s
|++++++++ | 15% ~59s
|+++++++++ | 16% ~21m 41s
|+++++++++ | 17% ~20m 02s
|++++++++++ | 18% ~18m 35s
|++++++++++ | 20% ~17m 18s
|+++++++++++ | 21% ~16m 09s
|+++++++++++ | 22% ~15m 08s
|++++++++++++ | 23% ~14m 13s
|+++++++++++++ | 24% ~13m 22s
|+++++++++++++ | 25% ~12m 37s
|++++++++++++++ | 26% ~11m 55s
|++++++++++++++ | 28% ~11m 16s
|+++++++++++++++ | 29% ~10m 41s
|+++++++++++++++ | 30% ~10m 09s
|++++++++++++++++ | 31% ~09m 38s
|+++++++++++++++++ | 32% ~09m 10s
|+++++++++++++++++ | 33% ~08m 44s
|++++++++++++++++++ | 34% ~08m 19s
|++++++++++++++++++ | 36% ~07m 56s
|+++++++++++++++++++ | 37% ~07m 34s
|+++++++++++++++++++ | 38% ~07m 14s
|++++++++++++++++++++ | 39% ~06m 55s
|+++++++++++++++++++++ | 40% ~06m 36s
|+++++++++++++++++++++ | 41% ~06m 19s
|++++++++++++++++++++++ | 43% ~06m 03s
|++++++++++++++++++++++ | 44% ~05m 47s
|+++++++++++++++++++++++ | 45% ~05m 32s
|+++++++++++++++++++++++ | 46% ~05m 18s
|++++++++++++++++++++++++ | 47% ~05m 05s
|+++++++++++++++++++++++++ | 48% ~04m 52s
|+++++++++++++++++++++++++ | 49% ~04m 40s
|++++++++++++++++++++++++++ | 51% ~04m 28s
|++++++++++++++++++++++++++ | 52% ~04m 17s
|+++++++++++++++++++++++++++ | 53% ~04m 06s
|++++++++++++++++++++++++++++ | 54% ~03m 56s
|++++++++++++++++++++++++++++ | 55% ~03m 46s
|+++++++++++++++++++++++++++++ | 56% ~03m 36s
|+++++++++++++++++++++++++++++ | 57% ~03m 27s
|++++++++++++++++++++++++++++++ | 59% ~03m 18s
|++++++++++++++++++++++++++++++ | 60% ~03m 09s
|+++++++++++++++++++++++++++++++ | 61% ~03m 01s
|++++++++++++++++++++++++++++++++ | 62% ~02m 53s
|++++++++++++++++++++++++++++++++ | 63% ~02m 45s
|+++++++++++++++++++++++++++++++++ | 64% ~02m 37s
|+++++++++++++++++++++++++++++++++ | 66% ~02m 30s
|++++++++++++++++++++++++++++++++++ | 67% ~02m 23s
|++++++++++++++++++++++++++++++++++ | 68% ~02m 16s
|+++++++++++++++++++++++++++++++++++ | 69% ~02m 09s
|++++++++++++++++++++++++++++++++++++ | 70% ~02m 03s
|++++++++++++++++++++++++++++++++++++ | 71% ~01m 57s
|+++++++++++++++++++++++++++++++++++++ | 72% ~01m 50s
|+++++++++++++++++++++++++++++++++++++ | 74% ~01m 44s
|++++++++++++++++++++++++++++++++++++++ | 75% ~01m 39s
|++++++++++++++++++++++++++++++++++++++ | 76% ~01m 33s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~01m 27s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~01m 22s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~01m 17s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~01m 12s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~01m 07s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~01m 02s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~57s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~52s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~48s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~43s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~39s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~35s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~31s
|++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~27s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~22s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~19s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~15s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~04s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=05m 08sCalculating cluster 5
| | 0 % ~calculating
|+ | 1 % ~02m 06s
|++ | 2 % ~01m 59s
|++ | 3 % ~01m 56s
|+++ | 4 % ~01m 55s
|+++ | 5 % ~01m 53s
|++++ | 6 % ~01m 52s
|++++ | 7 % ~01m 51s
|+++++ | 9 % ~01m 50s
|+++++ | 10% ~01m 49s
|++++++ | 11% ~01m 47s
|++++++ | 12% ~01m 46s
|+++++++ | 13% ~01m 45s
|+++++++ | 14% ~01m 43s
|++++++++ | 15% ~01m 42s
|++++++++ | 16% ~01m 40s
|+++++++++ | 17% ~01m 39s
|++++++++++ | 18% ~01m 37s
|++++++++++ | 19% ~01m 35s
|+++++++++++ | 20% ~01m 34s
|+++++++++++ | 21% ~01m 33s
|++++++++++++ | 22% ~01m 32s
|++++++++++++ | 23% ~01m 31s
|+++++++++++++ | 24% ~01m 29s
|+++++++++++++ | 26% ~01m 28s
|++++++++++++++ | 27% ~01m 27s
|++++++++++++++ | 28% ~01m 25s
|+++++++++++++++ | 29% ~01m 24s
|+++++++++++++++ | 30% ~01m 23s
|++++++++++++++++ | 31% ~01m 21s
|++++++++++++++++ | 32% ~01m 20s
|+++++++++++++++++ | 33% ~01m 19s
|++++++++++++++++++ | 34% ~01m 17s
|++++++++++++++++++ | 35% ~01m 16s
|+++++++++++++++++++ | 36% ~01m 15s
|+++++++++++++++++++ | 37% ~01m 14s
|++++++++++++++++++++ | 38% ~01m 12s
|++++++++++++++++++++ | 39% ~01m 11s
|+++++++++++++++++++++ | 40% ~01m 10s
|+++++++++++++++++++++ | 41% ~01m 08s
|++++++++++++++++++++++ | 43% ~01m 07s
|++++++++++++++++++++++ | 44% ~01m 06s
|+++++++++++++++++++++++ | 45% ~01m 05s
|+++++++++++++++++++++++ | 46% ~01m 04s
|++++++++++++++++++++++++ | 47% ~01m 03s
|++++++++++++++++++++++++ | 48% ~01m 01s
|+++++++++++++++++++++++++ | 49% ~60s
|+++++++++++++++++++++++++ | 50% ~59s
|++++++++++++++++++++++++++ | 51% ~57s
|+++++++++++++++++++++++++++ | 52% ~56s
|+++++++++++++++++++++++++++ | 53% ~55s
|++++++++++++++++++++++++++++ | 54% ~54s
|++++++++++++++++++++++++++++ | 55% ~52s
|+++++++++++++++++++++++++++++ | 56% ~51s
|+++++++++++++++++++++++++++++ | 57% ~50s
|++++++++++++++++++++++++++++++ | 59% ~48s
|++++++++++++++++++++++++++++++ | 60% ~47s
|+++++++++++++++++++++++++++++++ | 61% ~46s
|+++++++++++++++++++++++++++++++ | 62% ~45s
|++++++++++++++++++++++++++++++++ | 63% ~43s
|++++++++++++++++++++++++++++++++ | 64% ~42s
|+++++++++++++++++++++++++++++++++ | 65% ~41s
|+++++++++++++++++++++++++++++++++ | 66% ~40s
|++++++++++++++++++++++++++++++++++ | 67% ~38s
|+++++++++++++++++++++++++++++++++++ | 68% ~37s
|+++++++++++++++++++++++++++++++++++ | 69% ~36s
|++++++++++++++++++++++++++++++++++++ | 70% ~35s
|++++++++++++++++++++++++++++++++++++ | 71% ~33s
|+++++++++++++++++++++++++++++++++++++ | 72% ~32s
|+++++++++++++++++++++++++++++++++++++ | 73% ~31s
|++++++++++++++++++++++++++++++++++++++ | 74% ~30s
|++++++++++++++++++++++++++++++++++++++ | 76% ~28s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~27s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~26s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~25s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~23s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~22s
|+++++++++++++++++++++++++++++++++++++++++ | 82% ~21s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~20s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~18s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~17s
|++++++++++++++++++++++++++++++++++++++++++++ | 86% ~16s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~15s
|+++++++++++++++++++++++++++++++++++++++++++++ | 88% ~14s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~12s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~11s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~05s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 54sCalculating cluster 6
| | 0 % ~calculating
|+ | 1 % ~01m 33s
|++ | 2 % ~01m 27s
|++ | 3 % ~01m 26s
|+++ | 4 % ~01m 23s
|+++ | 6 % ~01m 21s
|++++ | 7 % ~01m 21s
|++++ | 8 % ~01m 20s
|+++++ | 9 % ~01m 20s
|++++++ | 10% ~01m 18s
|++++++ | 11% ~01m 17s
|+++++++ | 12% ~01m 16s
|+++++++ | 13% ~01m 15s
|++++++++ | 15% ~01m 14s
|++++++++ | 16% ~01m 12s
|+++++++++ | 17% ~01m 11s
|+++++++++ | 18% ~01m 10s
|++++++++++ | 19% ~01m 09s
|+++++++++++ | 20% ~01m 08s
|+++++++++++ | 21% ~01m 07s
|++++++++++++ | 22% ~01m 06s
|++++++++++++ | 24% ~01m 05s
|+++++++++++++ | 25% ~01m 04s
|+++++++++++++ | 26% ~01m 03s
|++++++++++++++ | 27% ~01m 02s
|+++++++++++++++ | 28% ~01m 01s
|+++++++++++++++ | 29% ~01m 00s
|++++++++++++++++ | 30% ~01m 00s
|++++++++++++++++ | 31% ~59s
|+++++++++++++++++ | 33% ~58s
|+++++++++++++++++ | 34% ~57s
|++++++++++++++++++ | 35% ~56s
|++++++++++++++++++ | 36% ~55s
|+++++++++++++++++++ | 37% ~54s
|++++++++++++++++++++ | 38% ~53s
|++++++++++++++++++++ | 39% ~52s
|+++++++++++++++++++++ | 40% ~51s
|+++++++++++++++++++++ | 42% ~50s
|++++++++++++++++++++++ | 43% ~49s
|++++++++++++++++++++++ | 44% ~48s
|+++++++++++++++++++++++ | 45% ~47s
|++++++++++++++++++++++++ | 46% ~46s
|++++++++++++++++++++++++ | 47% ~45s
|+++++++++++++++++++++++++ | 48% ~44s
|+++++++++++++++++++++++++ | 49% ~43s
|++++++++++++++++++++++++++ | 51% ~42s
|++++++++++++++++++++++++++ | 52% ~41s
|+++++++++++++++++++++++++++ | 53% ~40s
|+++++++++++++++++++++++++++ | 54% ~39s
|++++++++++++++++++++++++++++ | 55% ~39s
|+++++++++++++++++++++++++++++ | 56% ~38s
|+++++++++++++++++++++++++++++ | 57% ~37s
|++++++++++++++++++++++++++++++ | 58% ~36s
|++++++++++++++++++++++++++++++ | 60% ~35s
|+++++++++++++++++++++++++++++++ | 61% ~34s
|+++++++++++++++++++++++++++++++ | 62% ~33s
|++++++++++++++++++++++++++++++++ | 63% ~32s
|+++++++++++++++++++++++++++++++++ | 64% ~31s
|+++++++++++++++++++++++++++++++++ | 65% ~30s
|++++++++++++++++++++++++++++++++++ | 66% ~29s
|++++++++++++++++++++++++++++++++++ | 67% ~28s
|+++++++++++++++++++++++++++++++++++ | 69% ~27s
|+++++++++++++++++++++++++++++++++++ | 70% ~26s
|++++++++++++++++++++++++++++++++++++ | 71% ~25s
|++++++++++++++++++++++++++++++++++++ | 72% ~24s
|+++++++++++++++++++++++++++++++++++++ | 73% ~23s
|++++++++++++++++++++++++++++++++++++++ | 74% ~22s
|++++++++++++++++++++++++++++++++++++++ | 75% ~21s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~20s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~19s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~18s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~17s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~16s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~15s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~14s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~13s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~11s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~09s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~08s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~06s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~03s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~02s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=01m 23sCalculating cluster 7
| | 0 % ~calculating
|+ | 1 % ~01m 12s
|++ | 2 % ~01m 11s
|++ | 3 % ~01m 12s
|+++ | 4 % ~01m 10s
|+++ | 6 % ~01m 09s
|++++ | 7 % ~01m 09s
|++++ | 8 % ~01m 09s
|+++++ | 9 % ~01m 08s
|++++++ | 10% ~01m 06s
|++++++ | 11% ~01m 06s
|+++++++ | 12% ~01m 05s
|+++++++ | 13% ~01m 04s
|++++++++ | 15% ~01m 04s
|++++++++ | 16% ~01m 03s
|+++++++++ | 17% ~01m 02s
|+++++++++ | 18% ~01m 01s
|++++++++++ | 19% ~01m 00s
|+++++++++++ | 20% ~59s
|+++++++++++ | 21% ~59s
|++++++++++++ | 22% ~58s
|++++++++++++ | 24% ~59s
|+++++++++++++ | 25% ~58s
|+++++++++++++ | 26% ~58s
|++++++++++++++ | 27% ~58s
|+++++++++++++++ | 28% ~57s
|+++++++++++++++ | 29% ~56s
|++++++++++++++++ | 30% ~55s
|++++++++++++++++ | 31% ~01m 01s
|+++++++++++++++++ | 33% ~59s
|+++++++++++++++++ | 34% ~58s
|++++++++++++++++++ | 35% ~57s
|++++++++++++++++++ | 36% ~56s
|+++++++++++++++++++ | 37% ~54s
|++++++++++++++++++++ | 38% ~53s
|++++++++++++++++++++ | 39% ~52s
|+++++++++++++++++++++ | 40% ~51s
|+++++++++++++++++++++ | 42% ~50s
|++++++++++++++++++++++ | 43% ~49s
|++++++++++++++++++++++ | 44% ~47s
|+++++++++++++++++++++++ | 45% ~46s
|++++++++++++++++++++++++ | 46% ~45s
|++++++++++++++++++++++++ | 47% ~44s
|+++++++++++++++++++++++++ | 48% ~43s
|+++++++++++++++++++++++++ | 49% ~42s
|++++++++++++++++++++++++++ | 51% ~41s
|++++++++++++++++++++++++++ | 52% ~40s
|+++++++++++++++++++++++++++ | 53% ~39s
|+++++++++++++++++++++++++++ | 54% ~38s
|++++++++++++++++++++++++++++ | 55% ~37s
|+++++++++++++++++++++++++++++ | 56% ~36s
|+++++++++++++++++++++++++++++ | 57% ~35s
|++++++++++++++++++++++++++++++ | 58% ~34s
|++++++++++++++++++++++++++++++ | 60% ~33s
|+++++++++++++++++++++++++++++++ | 61% ~32s
|+++++++++++++++++++++++++++++++ | 62% ~31s
|++++++++++++++++++++++++++++++++ | 63% ~30s
|+++++++++++++++++++++++++++++++++ | 64% ~29s
|+++++++++++++++++++++++++++++++++ | 65% ~28s
|++++++++++++++++++++++++++++++++++ | 66% ~27s
|++++++++++++++++++++++++++++++++++ | 67% ~26s
|+++++++++++++++++++++++++++++++++++ | 69% ~25s
|+++++++++++++++++++++++++++++++++++ | 70% ~24s
|++++++++++++++++++++++++++++++++++++ | 71% ~24s
|++++++++++++++++++++++++++++++++++++ | 72% ~23s
|+++++++++++++++++++++++++++++++++++++ | 73% ~22s
|++++++++++++++++++++++++++++++++++++++ | 74% ~21s
|++++++++++++++++++++++++++++++++++++++ | 75% ~20s
|+++++++++++++++++++++++++++++++++++++++ | 76% ~19s
|+++++++++++++++++++++++++++++++++++++++ | 78% ~18s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~17s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~16s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~15s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~14s
|++++++++++++++++++++++++++++++++++++++++++ | 83% ~13s
|+++++++++++++++++++++++++++++++++++++++++++ | 84% ~13s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~12s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~11s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~09s
|+++++++++++++++++++++++++++++++++++++++++++++ | 90% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~07s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~05s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 94% ~44s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~34s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~26s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~17s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=12m 16sCalculating cluster 8
| | 0 % ~calculating
|+ | 1 % ~01m 55s
|++ | 2 % ~01m 57s
|++ | 3 % ~01m 52s
|+++ | 4 % ~01m 52s
|+++ | 5 % ~01m 50s
|++++ | 7 % ~01m 51s
|++++ | 8 % ~01m 49s
|+++++ | 9 % ~01m 48s
|+++++ | 10% ~01m 46s
|++++++ | 11% ~01m 45s
|+++++++ | 12% ~01m 44s
|+++++++ | 13% ~01m 42s
|++++++++ | 14% ~01m 41s
|++++++++ | 15% ~01m 40s
|+++++++++ | 16% ~01m 38s
|+++++++++ | 18% ~01m 39s
|++++++++++ | 19% ~01m 38s
|++++++++++ | 20% ~01m 36s
|+++++++++++ | 21% ~01m 35s
|+++++++++++ | 22% ~01m 33s
|++++++++++++ | 23% ~01m 32s
|+++++++++++++ | 24% ~01m 30s
|+++++++++++++ | 25% ~01m 29s
|++++++++++++++ | 26% ~01m 27s
|++++++++++++++ | 27% ~01m 26s
|+++++++++++++++ | 29% ~01m 24s
|+++++++++++++++ | 30% ~01m 23s
|++++++++++++++++ | 31% ~01m 21s
|++++++++++++++++ | 32% ~01m 20s
|+++++++++++++++++ | 33% ~01m 19s
|++++++++++++++++++ | 34% ~01m 17s
|++++++++++++++++++ | 35% ~01m 16s
|+++++++++++++++++++ | 36% ~01m 15s
|+++++++++++++++++++ | 37% ~01m 13s
|++++++++++++++++++++ | 38% ~01m 12s
|++++++++++++++++++++ | 40% ~01m 10s
|+++++++++++++++++++++ | 41% ~01m 09s
|+++++++++++++++++++++ | 42% ~01m 08s
|++++++++++++++++++++++ | 43% ~01m 07s
|++++++++++++++++++++++ | 44% ~01m 05s
|+++++++++++++++++++++++ | 45% ~01m 04s
|++++++++++++++++++++++++ | 46% ~01m 03s
|++++++++++++++++++++++++ | 47% ~01m 02s
|+++++++++++++++++++++++++ | 48% ~01m 00s
|+++++++++++++++++++++++++ | 49% ~59s
|++++++++++++++++++++++++++ | 51% ~58s
|++++++++++++++++++++++++++ | 52% ~57s
|+++++++++++++++++++++++++++ | 53% ~55s
|+++++++++++++++++++++++++++ | 54% ~54s
|++++++++++++++++++++++++++++ | 55% ~53s
|+++++++++++++++++++++++++++++ | 56% ~51s
|+++++++++++++++++++++++++++++ | 57% ~50s
|++++++++++++++++++++++++++++++ | 58% ~49s
|++++++++++++++++++++++++++++++ | 59% ~48s
|+++++++++++++++++++++++++++++++ | 60% ~46s
|+++++++++++++++++++++++++++++++ | 62% ~45s
|++++++++++++++++++++++++++++++++ | 63% ~44s
|++++++++++++++++++++++++++++++++ | 64% ~42s
|+++++++++++++++++++++++++++++++++ | 65% ~41s
|+++++++++++++++++++++++++++++++++ | 66% ~40s
|++++++++++++++++++++++++++++++++++ | 67% ~38s
|+++++++++++++++++++++++++++++++++++ | 68% ~37s
|+++++++++++++++++++++++++++++++++++ | 69% ~36s
|++++++++++++++++++++++++++++++++++++ | 70% ~35s
|++++++++++++++++++++++++++++++++++++ | 71% ~33s
|+++++++++++++++++++++++++++++++++++++ | 73% ~32s
|+++++++++++++++++++++++++++++++++++++ | 74% ~31s
|++++++++++++++++++++++++++++++++++++++ | 75% ~30s
|++++++++++++++++++++++++++++++++++++++ | 76% ~29s
|+++++++++++++++++++++++++++++++++++++++ | 77% ~27s
|++++++++++++++++++++++++++++++++++++++++ | 78% ~26s
|++++++++++++++++++++++++++++++++++++++++ | 79% ~25s
|+++++++++++++++++++++++++++++++++++++++++ | 80% ~24s
|+++++++++++++++++++++++++++++++++++++++++ | 81% ~22s
|++++++++++++++++++++++++++++++++++++++++++ | 82% ~21s
|++++++++++++++++++++++++++++++++++++++++++ | 84% ~20s
|+++++++++++++++++++++++++++++++++++++++++++ | 85% ~19s
|+++++++++++++++++++++++++++++++++++++++++++ | 86% ~18s
|++++++++++++++++++++++++++++++++++++++++++++ | 87% ~17s
|++++++++++++++++++++++++++++++++++++++++++++ | 88% ~15s
|+++++++++++++++++++++++++++++++++++++++++++++ | 89% ~14s
|++++++++++++++++++++++++++++++++++++++++++++++ | 90% ~13s
|++++++++++++++++++++++++++++++++++++++++++++++ | 91% ~11s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 92% ~10s
|+++++++++++++++++++++++++++++++++++++++++++++++ | 93% ~08s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 95% ~07s
|++++++++++++++++++++++++++++++++++++++++++++++++ | 96% ~06s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 97% ~04s
|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 09sCalculating cluster 9
| | 0 % ~calculating
|+ | 1 % ~01m 58s
|++ | 2 % ~01m 60s
|++ | 3 % ~01m 58s
|+++ | 4 % ~01m 58s
|+++ | 5 % ~01m 58s
|++++ | 6 % ~01m 58s
|++++ | 8 % ~01m 57s
|+++++ | 9 % ~01m 56s
|+++++ | 10% ~01m 55s
|++++++ | 11% ~01m 54s
|++++++ | 12% ~01m 52s
|+++++++ | 13% ~01m 51s
|+++++++ | 14% ~01m 49s
|++++++++ | 15% ~01m 48s
|+++++++++ | 16% ~01m 47s
|+++++++++ | 17% ~01m 46s
|++++++++++ | 18% ~01m 45s
|++++++++++ | 19% ~01m 43s
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|+++++++++++ | 22% ~01m 40s
|++++++++++++ | 23% ~01m 40s
|++++++++++++ | 24% ~01m 39s
|+++++++++++++ | 25% ~01m 37s
|+++++++++++++ | 26% ~01m 35s
|++++++++++++++ | 27% ~01m 34s
|++++++++++++++ | 28% ~01m 32s
|+++++++++++++++ | 29% ~01m 31s
|++++++++++++++++ | 30% ~01m 30s
|++++++++++++++++ | 31% ~01m 28s
|+++++++++++++++++ | 32% ~01m 27s
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|++++++++++++++++++ | 34% ~01m 24s
|++++++++++++++++++ | 35% ~01m 23s
|+++++++++++++++++++ | 37% ~01m 21s
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|++++++++++++++++++++ | 39% ~01m 18s
|++++++++++++++++++++ | 40% ~01m 17s
|+++++++++++++++++++++ | 41% ~01m 15s
|+++++++++++++++++++++ | 42% ~01m 14s
|++++++++++++++++++++++ | 43% ~01m 13s
|+++++++++++++++++++++++ | 44% ~01m 11s
|+++++++++++++++++++++++ | 45% ~01m 10s
|++++++++++++++++++++++++ | 46% ~01m 09s
|++++++++++++++++++++++++ | 47% ~01m 07s
|+++++++++++++++++++++++++ | 48% ~01m 06s
|+++++++++++++++++++++++++ | 49% ~01m 05s
|++++++++++++++++++++++++++ | 51% ~01m 03s
|++++++++++++++++++++++++++ | 52% ~01m 02s
|+++++++++++++++++++++++++++ | 53% ~01m 01s
|+++++++++++++++++++++++++++ | 54% ~59s
|++++++++++++++++++++++++++++ | 55% ~58s
|++++++++++++++++++++++++++++ | 56% ~57s
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|++++++++++++++++++++++++++++++++ | 62% ~48s
|++++++++++++++++++++++++++++++++ | 63% ~47s
|+++++++++++++++++++++++++++++++++ | 65% ~45s
|+++++++++++++++++++++++++++++++++ | 66% ~44s
|++++++++++++++++++++++++++++++++++ | 67% ~42s
|++++++++++++++++++++++++++++++++++ | 68% ~41s
|+++++++++++++++++++++++++++++++++++ | 69% ~40s
|+++++++++++++++++++++++++++++++++++ | 70% ~38s
|++++++++++++++++++++++++++++++++++++ | 71% ~37s
|+++++++++++++++++++++++++++++++++++++ | 72% ~36s
|+++++++++++++++++++++++++++++++++++++ | 73% ~34s
|++++++++++++++++++++++++++++++++++++++ | 74% ~33s
|++++++++++++++++++++++++++++++++++++++ | 75% ~31s
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|++++++++++++++++++++++++++++++++++++++++ | 78% ~27s
|++++++++++++++++++++++++++++++++++++++++ | 80% ~26s
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|+++++++++++++++++++++++++++++++++++++++++ | 82% ~23s
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|+++++++++++++++++++++++++++++++++++++++++++++++++ | 98% ~03s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 07sCalculating cluster 10
| | 0 % ~calculating
|+ | 1 % ~02m 04s
|++ | 2 % ~02m 03s
|++ | 3 % ~02m 02s
|+++ | 4 % ~02m 00s
|+++ | 5 % ~01m 57s
|++++ | 6 % ~01m 58s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 03sCalculating cluster 11
| | 0 % ~calculating
|+ | 1 % ~01m 54s
|+ | 2 % ~01m 58s
|++ | 3 % ~01m 53s
|++ | 4 % ~01m 55s
|+++ | 5 % ~01m 56s
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02m 01slibrary(enrichR)Welcome to enrichR
Checking connection ...
Enrichr ... Connection is Live!
FlyEnrichr ... Connection is available!
WormEnrichr ... Connection is available!
YeastEnrichr ... Connection is available!
FishEnrichr ... Connection is available!
OxEnrichr ... Connection is available!setEnrichrSite("Enrichr") # Human genesConnection changed to https://maayanlab.cloud/Enrichr/
Connection is Live!# list of all the databases
# get the possible libraries
dbs <- listEnrichrDbs()
# this will list the possible libraries
dbs
# select libraries with cell types
db <- c('CellMarker_Augmented_2021','Azimuth_Cell_Types_2021')
# function for a quick look
checkCelltypes <- function(cluster_num = 0){
clusterX <- ClusterMarkers %>% filter(cluster == cluster_num & avg_log2FC > 0.25)
genes <- clusterX$gene
# the cell type libraries
# get the results for each library
clusterX.cell <- enrichr(genes, databases = db)
# visualize the results
print(plotEnrich(clusterX.cell[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value", title = 'CellMarker_Augmented_2021'))
print(plotEnrich(clusterX.cell[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value", title = 'Azimuth_Cell_Types_2021'))
}
Plot top markers
DoHeatmap(seu, features = top2$gene, size = 3, angle = 90, group.by = "integrated_snn_res.0.3")
Error in DoHeatmap(seu, features = top2$gene, size = 3, angle = 90, group.by = "integrated_snn_res.0.3") :
No requested features found in the scale.data slot for the RNA assay.Quick look
checkCelltypes(cluster_num = 11)Uploading data to Enrichr... Done.
Querying CellMarker_Augmented_2021... Done.
Querying Azimuth_Cell_Types_2021... Done.
Parsing results... Done.
Add cell types - fine resolution
Save this object.
Now get the aggregated data for differential expression
To run DGE we need to make the meta data table
library(DESeq2)
# Create an empty list to store the results for each cell type
list.results <- vector("list", length(Celltypes))
# Loop through each cell type and perform DESeq analysis
for (i in seq_along(Celltypes)) {
# Subset the expression dataframe by the current cell type
print(Celltypes[i])
df_sub <- df.trans[grepl(paste0("^", Celltypes[i], "_"), rownames(df.trans)), ]
print(dim(df_sub))
# test one cell group
#i = "NPC-div"
df.meta_sub <- df.meta[df.meta$Celltype == Celltypes[i], ]
print(dim(df.meta_sub))
# Prepare the DESeq object
dft <- as.data.frame(t(df_sub)) # Transpose the subset dataframe to get genes as rows and samples as columns
dfi <- lapply(dft, as.integer)
dfi <- as.data.frame(dfi)
rownames(dfi) <- rownames(dft)
# Create the DESeqDataSet object using the subset dataframe and metadata
dds <- DESeqDataSetFromMatrix(countData = dfi, colData = df.meta_sub, design = ~DiseaseStatus)
# Perform DESeq analysis
dds <- DESeq(dds)
# Store the DESeq results in the list with the cell type as the list index
res <- results(dds)
list2 <- list()
list2[["dds"]] <- dds
list2[["results"]] <- res
# Initialize an empty list to store the results for each contrast
all_results <- list()
# Loop through each contrast and calculate the results
for (j in 1:(length(all_contrasts) - 1)) {
for (k in (j + 1):length(all_contrasts)) {
contrast_level1 <- all_contrasts[j]
contrast_level2 <- all_contrasts[k]
# Check if both levels have at least one sample
if (any(dds$DiseaseStatus %in% c(contrast_level1, contrast_level2))) {
# Check if contrast levels are different
if (contrast_level1 != contrast_level2) {
contrast_name <- paste("DiseaseStatus", contrast_level1, "vs", contrast_level2)
# Filter rows with complete cases for the current contrast levels
complete_cases <- complete.cases(dds$DiseaseStatus, dds$DiseaseStatus %in% c(contrast_level1, contrast_level2))
# Subset the DESeq object and calculate the contrast results
dds_sub <- dds[complete_cases, ]
# Check if both levels still exist in the subset after removing missing values
if (contrast_level1 %in% unique(dds_sub$DiseaseStatus) && contrast_level2 %in% unique(dds_sub$DiseaseStatus)) {
contrast_result <- results(dds_sub, contrast = c("DiseaseStatus", contrast_level1, contrast_level2), name = contrast_name)
all_results[[contrast_name]] <- contrast_result
} else {
message(paste("Skipping", contrast_name, "due to missing contrast levels in DiseaseStatus."))
}
} else {
message(paste("Skipping", contrast_level1, "vs", contrast_level2, "since they are the same level."))
}
}
}
}
# Add the contrast results to the list
list2[["contrast_results"]] <- all_results
# Store the list for the current cell type in the appropriate slot
list.results[[i]] <- list2
}[1] "NPC-div"
[1] 22 58395
[1] 22 4Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factors
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
estimating size factors
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
final dispersion estimates
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
fitting model and testing
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
-- replacing outliers and refitting for 52 genes
-- DESeq argument 'minReplicatesForReplace' = 7
-- original counts are preserved in counts(dds)
estimating dispersions
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
fitting model and testing
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error][1] "NeuronGABA"
[1] 22 58395
[1] 22 4Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factors
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
estimating size factors
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
final dispersion estimates
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
fitting model and testing
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
-- replacing outliers and refitting for 72 genes
-- DESeq argument 'minReplicatesForReplace' = 7
-- original counts are preserved in counts(dds)
estimating dispersions
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
fitting model and testing
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error][1] "Progenitor"
[1] 21 58395
[1] 21 4Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factors
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
estimating size factors
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
final dispersion estimates
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
fitting model and testing
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
-- replacing outliers and refitting for 35 genes
-- DESeq argument 'minReplicatesForReplace' = 7
-- original counts are preserved in counts(dds)
estimating dispersions
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
fitting model and testing
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error][1] "Epithelial"
[1] 21 58395
[1] 21 4Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factors
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
estimating size factors
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
final dispersion estimates
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
fitting model and testing
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
-- replacing outliers and refitting for 82 genes
-- DESeq argument 'minReplicatesForReplace' = 7
-- original counts are preserved in counts(dds)
estimating dispersions
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
fitting model and testing
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error][1] "NPC-DA"
[1] 21 58395
[1] 21 4Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factors
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
estimating size factors
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
final dispersion estimates
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
fitting model and testing
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
-- replacing outliers and refitting for 154 genes
-- DESeq argument 'minReplicatesForReplace' = 7
-- original counts are preserved in counts(dds)
estimating dispersions
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
fitting model and testing
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error][1] "NeuronDA"
[1] 22 58395
[1] 22 4Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factors
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
estimating size factors
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
final dispersion estimates
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
fitting model and testing
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
-- replacing outliers and refitting for 171 genes
-- DESeq argument 'minReplicatesForReplace' = 7
-- original counts are preserved in counts(dds)
estimating dispersions
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
fitting model and testing
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error][1] "Neuron"
[1] 16 58395
[1] 16 4Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factors
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
estimating size factors
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
final dispersion estimates
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
fitting model and testing
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
-- replacing outliers and refitting for 54 genes
-- DESeq argument 'minReplicatesForReplace' = 7
-- original counts are preserved in counts(dds)
estimating dispersions
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
fitting model and testing
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error][1] "Stem"
[1] 17 58395
[1] 17 4Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factors
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
estimating size factors
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
final dispersion estimates
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
fitting model and testing
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
-- replacing outliers and refitting for 48 genes
-- DESeq argument 'minReplicatesForReplace' = 7
-- original counts are preserved in counts(dds)
estimating dispersions
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
fitting model and testing
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error][1] "Endothelial"
[1] 8 58395
[1] 8 4Warning in DESeqDataSet(se, design = design, ignoreRank) :
some variables in design formula are characters, converting to factors
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
estimating size factors
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
final dispersion estimates
Note: levels of factors in the design contain characters other than
letters, numbers, '_' and '.'. It is recommended (but not required) to use
only letters, numbers, and delimiters '_' or '.', as these are safe characters
for column names in R. [This is a message, not a warning or an error]
fitting model and testingSame loop that should add the cell type names
# Create an empty list to store the results for each cell type
list.results <- setNames(vector("list", length(Celltypes)), Celltypes)
# Loop through each cell type and perform DESeq analysis
for (i in seq_along(Celltypes)) {
# Subset the expression dataframe by the current cell type
print(Celltypes[i])
df_sub <- df.trans[grepl(paste0("^", Celltypes[i], "_"), rownames(df.trans)), ]
print(dim(df_sub))
# test one cell group
#i = "NPC-div"
df.meta_sub <- df.meta[df.meta$Celltype == Celltypes[i], ]
print(dim(df.meta_sub))
# Prepare the DESeq object
dft <- as.data.frame(t(df_sub)) # Transpose the subset dataframe to get genes as rows and samples as columns
dfi <- lapply(dft, as.integer)
dfi <- as.data.frame(dfi)
rownames(dfi) <- rownames(dft)
# Create the DESeqDataSet object using the subset dataframe and metadata
dds <- DESeqDataSetFromMatrix(countData = dfi, colData = df.meta_sub, design = ~DiseaseStatus)
# Perform DESeq analysis
dds <- DESeq(dds)
# Store the DESeq results in the list with the cell type as the list index
res <- results(dds)
list2 <- list()
list2[["dds"]] <- dds
list2[["results"]] <- res
# Initialize an empty list to store the results for each contrast
all_results <- list()
# Loop through each contrast and calculate the results
for (j in 1:(length(all_contrasts) - 1)) {
for (k in (j + 1):length(all_contrasts)) {
contrast_level1 <- all_contrasts[j]
contrast_level2 <- all_contrasts[k]
# Check if both levels have at least one sample
if (any(dds$DiseaseStatus %in% c(contrast_level1, contrast_level2))) {
# Check if contrast levels are different
if (contrast_level1 != contrast_level2) {
contrast_name <- paste("DiseaseStatus", contrast_level1, "vs", contrast_level2)
# Filter rows with complete cases for the current contrast levels
complete_cases <- complete.cases(dds$DiseaseStatus, dds$DiseaseStatus %in% c(contrast_level1, contrast_level2))
# Subset the DESeq object and calculate the contrast results
dds_sub <- dds[complete_cases, ]
# Check if both levels still exist in the subset after removing missing values
if (contrast_level1 %in% unique(dds_sub$DiseaseStatus) && contrast_level2 %in% unique(dds_sub$DiseaseStatus)) {
contrast_result <- results(dds_sub, contrast = c("DiseaseStatus", contrast_level1, contrast_level2), name = contrast_name)
all_results[[contrast_name]] <- contrast_result
} else {
message(paste("Skipping", contrast_name, "due to missing contrast levels in DiseaseStatus."))
}
} else {
message(paste("Skipping", contrast_level1, "vs", contrast_level2, "since they are the same level."))
}
}
}
}
# Add the contrast results to the list
list2[["contrast_results"]] <- all_results
# Store the list for the current cell type in the appropriate slot with the cell type name
list.results[[Celltypes[i]]] <- list2
}
# Create an empty list to store the results for each cell type and contrast
# Create an empty list to store the results for each cell type
list.results <- list()
# Loop through each cell type and perform DESeq analysis
# Create an empty list to store the results for each cell type
list.results <- vector("list", length(Celltypes))
# Loop through each cell type and perform DESeq analysis
for (i in seq_along(Celltypes)) {
# ... (your previous code to subset and prepare the DESeq object)
# Initialize an empty list to store the results for each contrast
all_results <- list()
# Loop through each contrast and calculate the results
for (i in 1:(length(all_contrasts) - 1)) {
for (j in (i + 1):length(all_contrasts)) {
contrast_level1 <- all_contrasts[i]
contrast_level2 <- all_contrasts[j]
# Check if both levels have at least one sample
if (any(dds$DiseaseStatus %in% c(contrast_level1, contrast_level2))) {
# Check if contrast levels are different
if (contrast_level1 != contrast_level2) {
contrast_name <- paste("DiseaseStatus", contrast_level1, "vs", contrast_level2)
# Filter rows with complete cases for the current contrast levels
complete_cases <- complete.cases(dds$DiseaseStatus, dds$DiseaseStatus %in% c(contrast_level1, contrast_level2))
# Subset the DESeq object and calculate the contrast results
dds_sub <- dds[complete_cases, ]
# Check if both levels still exist in the subset after removing missing values
if (contrast_level1 %in% unique(dds_sub$DiseaseStatus) && contrast_level2 %in% unique(dds_sub$DiseaseStatus)) {
contrast_result <- results(dds_sub, contrast = c("DiseaseStatus", contrast_level1, contrast_level2), name = contrast_name)
all_results[[contrast_name]] <- contrast_result
} else {
message(paste("Skipping", contrast_name, "due to missing contrast levels in DiseaseStatus."))
}
} else {
message(paste("Skipping", contrast_level1, "vs", contrast_level2, "since they are the same level."))
}
}
}
}
# Store the DESeq results and objects in the list
list.results[[i]] <- list(
dds = dds,
results = all_results
)
}
Summarize the DGEs
print(summary_table)
#"/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Experiment1-mini12/SumCounts_NPC_from48sample.csv"
write.csv(summary_table,"/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Experiment1-mini12/DGE_summary_counts_NPCcultures.csv")
saveRDS(list.results, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Experiment1-mini12/DGE_NPCculturesList.RDS")library(EnhancedVolcano)
EnhancedVolcano(res.NPC,
lab = rownames(res.NPC),
x = 'log2FoldChange',
y = 'pvalue',
#ylim = c(0,20),
#xlim = c(-2,2),
pCutoff = 0.001,
FCcutoff = 1,
pointSize = 5.0,
labSize = 5,
#legendLabSize = 20,
#subtitleLabSize = 20,
#legendIconSize = 10,
) 
---
title: "R Notebook"
output: html_notebook
---

```{r}
library(Seurat)
library(tidyverse)
#library(CelltypeR)
```



```{r}
seu <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/Parse48_analysisMaria.RDS")

unique(seu$sample)

Idents(seu) <- "sample"
levels(seu)

samples <- levels(seu)
samples

```

Add meta data information

```{r}
# using celltypeR function

# add the culture type NPC, DA neurons 2weeks, Motor neurons
new_vector <- ifelse(grepl("NPC", samples), "NPC",
                     ifelse(grepl("Final", samples), "Neurons2weeks",
                            ifelse(grepl("^M", samples), "MotorNeurons", "Other")))

# Print the new vector
print(new_vector)

seu <- annotate(seu, annotations = new_vector, to_label = "sample",annotation_name = "CultureType")

DimPlot(seu)

# check with sample names

table(seu$sample, seu$CultureType)


```

Add the lines and then the diseases status

```{r}

  # Create a new vector with line names
new_line_vector <- gsub("^(NPC|Final)", "", samples)
new_line_vector <- gsub("3123$", "3123-iso", new_line_vector)
new_line_vector[new_line_vector == "P65P"] <- "3123-isoP65P"
new_line_vector[new_line_vector == "Q65Q"] <- "3123-isoQ65Q"

# Print the new vector with line names


new_line_vector[new_line_vector == "2AIW002-02"] <- "AIW002-02"

print(new_line_vector)

seu <- annotate(seu, annotations = new_line_vector, to_label = "sample",annotation_name = "Line")


# check
table(seu$sample, seu$Line)

```
```{r, fig.width=6}
DimPlot(seu)
```


```{r}
# add disease status

# Create the lookup table for Line to DiseaseStatus mapping
line_to_disease <- c("3448" = "HC",
                     "TD22" = "HC",
                     "AIW001-02" = "HC",
                     "AIW002-02" = "HC",
                     "2965" = "PD",
                     "3383" = "PD",
                     "3575" = "PD",
                     "TD06" = "PD",
                     "TD07" = "PD",
                     "3123" = "PD",
                     "3567" = "PD",
                     "3595" = "PD",
                     "3940" = "PD",
                     "3567-iso" = "PD-iso",
                     "3940R-iso" = "PD-iso",
                     "TD06-iso" = "PD-iso",
                     "3940N-iso" = "PD-iso",
                     "3123-isoQ65Q" = "PD-iso",
                     "3123-isoP65P" = "PD-iso",
                     "3123-iso" = "PD-iso",
                     "3383-iso" = "PD-iso",
                     "3575-iso" = "PD-iso",
                     "3595-iso" = "PD-iso")

# Assuming "Line" is your starting character vector
Idents(seu) <- "Line"
Line <- levels(seu)
# Create a new vector of disease statuses using the lookup table
disease_status_vector <- ifelse(Line %in% names(line_to_disease), 
                                line_to_disease[Line], 
                                "other")

# Print the new vector of disease statuses
print(disease_status_vector)

seu <- annotate(seu, annotations = disease_status_vector, to_label = "Line",annotation_name = "DiseaseStatus")


# check
table(seu$Line, seu$DiseaseStatus)


```

```{r}
cellcounts <- as.data.frame(table(seu$Line,seu$CultureType))
table(seu$Line,seu$CultureType,seu$DiseaseStatus)

# Convert long_df to the wide format
wide_df <- pivot_wider(cellcounts, 
                       id_cols = Var1, 
                       names_from = Var2, 
                       values_from = Freq)

write.csv(wide_df,"cellcounts_sample.csv")

```

```{r}
DimPlot(seu, group.by = "DiseaseStatus")

```




Subset for to remove pools and Maria's samples

```{r}
unique(seu$sample)
Idents(seu) <- "DiseaseStatus"

seu.n <- subset(seu, idents = c("HC","PD","PD-iso"))
dim(seu.n)
dim(seu)

DimPlot(seu.n)

```
Reprocess subset

```{r}

seu.n <- RunPCA(seu.n, npcs = 20, verbose = FALSE)
seu.n <- RunUMAP(seu.n, reduction = "pca", dims = 1:20, n.neighbors = 81)
DimPlot(seu.n)
seu.n$CultureType
DimPlot(seu.n, group.by = "CultureType")
DimPlot(seu.n, group.by = "Line")

```

```{r}
seu.n <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamples.RDS")
  
```



Get clusters

```{r}
seu.n <- NormalizeData(seu.n)
seu.n <- FindVariableFeatures(seu.n)
seu.n <- FindNeighbors(seu.n, dims = 1:20, k.param = 81)
seu.n <- FindClusters(seu.n, resolution = 0.3)
DimPlot(seu.n, group.by = "RNA_snn_res.0.3")
#seu.n$RNA_snn_res.0.3
DimPlot(seu.n, group.by = "CultureType")

```





Subset out neurons and NPC

```{r}

#seu.n$CultureType
Idents(seu.n) <- "CultureType"
neurons <- subset(seu.n, idents = "Neurons2weeks")

Idents(seu.n) <- "CultureType"
NPC <- subset(seu.n, idents = "NPC")

```

```{r}

seu <- neurons
seu <- RunPCA(seu, npcs = 20, verbose = FALSE)
seu <- RunUMAP(seu, reduction = "pca", dims = 1:20, n.neighbors = 81)
seu <- FindVariableFeatures(seu)
seu <- FindNeighbors(seu, dims = 1:20, k.param = 81)
seu <- FindClusters(seu, resolution = 0.3)
DimPlot(seu)
DimPlot(seu, group.by = "CultureType")
DimPlot(seu, group.by = "Line")

neurons <- seu

```

Integration

```{r}



```

```{r}
seu <- NPC
seu <- RunPCA(seu, npcs = 20, verbose = FALSE)
seu <- RunUMAP(seu, reduction = "pca", dims = 1:20, n.neighbors = 81)
seu <- FindVariableFeatures(seu)
seu <- FindNeighbors(seu, dims = 1:20, k.param = 81)
seu <- FindClusters(seu, resolution = 0.3)
DimPlot(seu)
DimPlot(seu, group.by = "CultureType")
DimPlot(seu, group.by = "Line")

NPC <- seu

DimPlot(NPC, group.by = "DiseaseStatus")

```



save objects 
```{r}

saveRDS(seu, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/Parse48_allSamples.RDS")

saveRDS(seu.n, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamples.RDS")
saveRDS(neurons, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNeurons.RDS")
saveRDS(NPC, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNPC.RDS")


NPC <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNPC.RDS")


```

```{r}
table(NPC$Line)
Idents(NPC) <- "Line"
levels(NPC)
table(NPC$DiseaseStatus,NPC$Line)

```

```{r}


```



Now integrate samples

```{r}

# All samples is too large to integrate 

table(NPC$Line)
# subset out the each disease type and then just merge
Idents(NPC) <- "DiseaseStatus"
levels(NPC)
NPC.PD <- subset(NPC, idents = "PD")
Idents(NPC.PD) <- "DiseaseStatus"
levels(NPC.PD)
Idents(NPC) <- "DiseaseStatus"
NPC.PDiso <- subset(NPC, idents = "PD-iso")
dim(NPC.PDiso)
Idents(NPC.PDiso) <- "DiseaseStatus"
levels(NPC.PDiso)
Idents(NPC) <- "DiseaseStatus"
NPC.HC <- subset(NPC, idents = "HC")
Idents(NPC.HC) <- "DiseaseStatus"
levels(NPC.HC)
Idents(NPC.HC) <- "Line"
levels(NPC.HC)
# make a list of seurat objects by our cell type variable
# will integrate the PD, PD-iso, HC separately and then merge them will see if will integrate or not

# PD
sublist <- SplitObject(NPC.PD, split.by = "Line")
# normalize and find variable features
for (i in 1:length(sublist)){
  sublist[[i]] <- NormalizeData(sublist[[i]], verbose = FALSE)
  sublist[[i]] <- FindVariableFeatures(sublist[[i]], selection.method = "vst")
}
# Create an empty Seurat object to store the integrated data
# Take the first Seurat object from the list as the starting point
integrated_seurat <- subset(sublist[[1]])
# Iterate over the list of Seurat objects
for (i in 1:length(sublist)) {
  # Rename the 'orig.ident' metadata inside the seurat object to match the object name in the list
  sublist[[i]]$orig.ident <- names(sublist)[i]

}

sample.list <- sublist
for (i in 1:length(sample.list)) {
  # Normalize and scale the data
  sample.list[[i]] <- NormalizeData(sample.list[[i]], verbose = FALSE)
  sample.list[[i]] <- ScaleData(sample.list[[i]], verbose = FALSE)
  # Find variable features
  sample.list[[i]] <- FindVariableFeatures(sample.list[[i]], selection.method = "vst")
  # Get the variable features
  variable_features <- VariableFeatures(sample.list[[i]])
  # Run PCA with the variable features
  sample.list[[i]] <- RunPCA(sample.list[[i]], verbose = FALSE, npcs = 30, features = variable_features)
}
int.anchors <- FindIntegrationAnchors(object.list = sample.list, dims = 1:30, reduction = "rpca")
integrated_seurat <- IntegrateData(anchorset = int.anchors,  dims = 1:30)
int.PD <- integrated_seurat
Idents(int.PD) <- "Line"
levels(int.PD)




```

```{r}
# an integrate funcrion

# Define a function for integrating a list of Seurat objects
integrate_seurat_objects <- function(seurat_list, dims = 1:30) {
  # Normalize and find variable features for each object
  for (i in 1:length(seurat_list)) {
    seurat_list[[i]] <- NormalizeData(seurat_list[[i]], verbose = FALSE)
    seurat_list[[i]] <- FindVariableFeatures(seurat_list[[i]], selection.method = "vst")
  }

  # Create an empty Seurat object to store the integrated data
  integrated_seurat <- subset(seurat_list[[1]])

  # Iterate over the list of Seurat objects
  for (i in 1:length(seurat_list)) {
    # Rename the 'orig.ident' metadata inside the Seurat object to match the object name in the list
    seurat_list[[i]]$orig.ident <- names(seurat_list)[i]
  }

  sample.list <- seurat_list
  for (i in 1:length(sample.list)) {
    # Normalize and scale the data
    sample.list[[i]] <- NormalizeData(sample.list[[i]], verbose = FALSE)
    sample.list[[i]] <- ScaleData(sample.list[[i]], verbose = FALSE)
    # Find variable features
    sample.list[[i]] <- FindVariableFeatures(sample.list[[i]], selection.method = "vst")
    # Get the variable features
    variable_features <- VariableFeatures(sample.list[[i]])
    # Run PCA with the variable features
    sample.list[[i]] <- RunPCA(sample.list[[i]], verbose = FALSE, npcs = 30, features = variable_features)
  }

  # Find integration anchors
  int.anchors <- FindIntegrationAnchors(object.list = sample.list, dims = dims, reduction = "rpca")

  # Integrate data
  integrated_seurat <- IntegrateData(anchorset = int.anchors, dims = dims)

  return(integrated_seurat)
}



```

Use function to integrate the HC

```{r}

sublist <- SplitObject(NPC.HC, split.by = "Line")

int.HC <- integrate_seurat_objects(sublist, dims = 1:30)

sublist <- SplitObject(NPC.PDiso, split.by = "Line")

int.iso <- integrate_seurat_objects(sublist, dims = 1:30)



```



```{r}

# Create a list of the three Seurat objects
DefaultAssay(int.HC) <- "RNA"
DefaultAssay(int.PD) <- "RNA"
DefaultAssay(int.PDiso) <- "RNA"
NPC_list <- list("HC" = int.HC,"PD" = int.PD,"PD-iso" = int.PDiso)

#listPDHC <- list("HC" = int.HC, "PD" = int.PD)
# Integrate the Seurat objects
integrated_seurat <- integrate_seurat_objects(NPC_list, dims = 1:20)

#integrated_seurat <- integrate_seurat_objects(listPDHC, dims = 1:30)



```

```{r}
# save integrated objects
saveRDS(int.HC, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNPCintHC.RDS")
saveRDS(int.PD, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNPCintPD.RDS")
saveRDS(int.PDiso, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNPCintPDiso.RDS")


int.PD <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNPCintPD.RDS")
int.HC <-readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNPCintHC.RDS")
int.PDiso <- readRDS("/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesNPCintPDiso.RDS")


```




```{r}
# make UMAP from integrated
seu <- integrated_seurat
table(seu$DiseaseStatus)


seu <- ScaleData(seu)
seu <- RunPCA(seu, npcs = 20, verbose = FALSE)
seu <- RunUMAP(seu, reduction = "pca", dims = 1:20, n.neighbors = 81)
seu <- FindVariableFeatures(seu)
seu <- FindNeighbors(seu, dims = 1:20, k.param = 81)
seu <- FindClusters(seu, resolution = 0.3)
DimPlot(seu)
DimPlot(seu, group.by = "Line")
DimPlot(seu, group.by = "DiseaseStatus")


```


```{r}
saveRDS(seu,"/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/ParseNCADDsamplesintegratedNPCs.RDS")

```



Annotate the NPC clusters

1. Find markers and Check Enricher
2. Get marker list score and 




Marker list
```{r}

da_neurons <- c("TH","SLC6A3","SLC18A2","SOX6","NDNF","SNCG","ALDH1A1","CALB1","TACR2","SLC17A6","SLC32A1","OTX2","GRP","LPL","CCK","VIP")
NPC_orStemLike <- c("DCX","NEUROD1","TBR1","PCNA","MKI67","SOX2","NES","PAX6")
mature_neurons = c("RBFOX3","SYP","VAMP1","VAMP2","TUBB3","SYT1","BSN","HOMER1","SLC17A6")
excitatory_neurons = c("GRIA2","GRIA1","GRIA4","GRIN1","GRIN2B","GRIN2A","GRIN3A","GRIN3","GRIP1","CAMK2A")
inhbitory_neurons = c("GAD1","GAD2", "GAT1","PVALB","GABR2","GABR1","GBRR1","GABRB2","GABRB1","GABRB3","GABRA6","GABRA1","GABRA4","TRAK2")
astrocytes <- c("GFAP","S100B","AQP4","APOE", "SOX9","SLC1A3")
oligodendrocytes <- c("MBP","MOG","OLIG1","OLIG2","SOX10")

radial_glia <- c("PTPRC","AIF1","ADGRE1", "VIM", "TNC","PTPRZ1","FAM107A","HOPX","LIFR",
              "ITGB5","IL6ST","SLC1A3")
epithelial <- c("HES1","HES5","SOX2","SOX10","NES","CDH1","NOTCH1")

microglia <- c("IBA1","P2RY12","P2RY13","TREM119", "GPR34","SIGLECH","TREM2",
               "CX3CR1","FCRLS","OLFML3","HEXB","TGFBR1", "SALL1","MERTK",
               "PROS1")

features_list <- c("MKI67","SOX2","POU5F1","DLX2","PAX6","SOX9","HES1","NES","RBFOX3","MAP2","NCAM1","CD24","GRIA2","GRIN2B","GABBR1","GAD1","GAD2","GABRA1","GABRB2","TH","ALDH1A1","LMX1B","NR4A2","CORIN","CALB1","KCNJ6","CXCR4","ITGA6","SLC1A3","CD44","AQP4","S100B", "PDGFRA","OLIG2","MBP","CLDN11","VIM","VCAM1")

short_list <- c("MKI67","SOX9","HES1","NES","DLX2","RBFOX3","MAP2","TH","CALB1","KCNJ6","SLC1A3","CD44","AQP4","S100B","OLIG2","MBP","VIM")


gene_lists = list("DA_neurons" = da_neurons, "NPC" = NPC_orStemLike,
                  "Neurons" = mature_neurons, 
                  "Oligo" = oligodendrocytes, "RadialGlia" = radial_glia,
                  "Epithelial" = epithelial)

```


```{r}
#seu$integrated_snn_res.0.3
Idents(seu) <- "integrated_snn_res.0.3"
DefaultAssay(seu) <- "RNA"
# Assuming you have already defined your Seurat object "seu" and "da_neurons" gene list
# Add module scores for the "da_neurons" gene list

seu <- AddModuleScore(
  seu,
  features = da_neurons,
  pool = NULL,
  nbin = 100,
  ctrl = 4,
  k = FALSE
)

calculate_module_scores <- function(seurat_object, gene_lists) {
  result <- list()
  
  for (gene_list_name in names(gene_lists)) {
    # Add module scores for each gene list
    seurat_object <- AddModuleScore(
      seurat_object,
      features = gene_lists[[gene_list_name]],
      name = gene_list_name,
      pool = NULL,
      nbin = 100,
      ctrl = 4,
      k = FALSE
    )
    
    # Extract the module scores for each cluster
    module_scores <- seurat_object@assays[gene_list_name]
    cluster_names <- colnames(module_scores)
    
    # Convert non-numeric values to NA
    module_scores[!is.numeric(module_scores)] <- NA
    
    # Calculate the mean module score for each cluster
    cluster_scores <- rowMeans(as.matrix(module_scores), na.rm = TRUE)
    
    # Create a data frame to store the scores for this gene list
    gene_list_scores <- data.frame(Cluster = cluster_names, Score = cluster_scores)
    
    # Store the gene list scores in the result list
    result[[gene_list_name]] <- gene_list_scores
  }
  
  return(result)
}

# Assuming you have already defined your Seurat object "seu" and "gene_lists"
# Calculate module scores for each gene list in "gene_lists"
module_scores_per_gene_list <- calculate_module_scores(seu, gene_lists)


# Combine the module scores into a single table
module_scores_table <- do.call(rbind, module_scores_per_gene_list)

# Print the table
print(module_scores_table)



```

```{r}
Idents(seu) <- "integrated_snn_res.0.3"

for (i in da_neurons) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}

```

```{r}
Idents(seu) <- "integrated_snn_res.0.3"

for (i in NPC_orStemLike) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
```
```{r}
Idents(seu) <- "integrated_snn_res.0.3"

for (i in mature_neurons) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}

```

```{r}
DimPlot(seu)
```

```{r}
Idents(seu) <- "integrated_snn_res.0.3"

for (i in astrocytes) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}

```

```{r}
Idents(seu) <- "integrated_snn_res.0.3"

for (i in radial_glia) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}

```


```{r}
Idents(seu) <- "integrated_snn_res.0.3"

for (i in epithelial) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
```
```{r}
Idents(seu) <- "integrated_snn_res.0.3"

for (i in oligodendrocytes) {
  print(FeaturePlot(seu, features = i, min.cutoff = 'q1', max.cutoff = 'q97', label = TRUE))
}
```
Find Markers

```{r}
ClusterMarkers <- FindAllMarkers(seu, only.pos = TRUE)

```


```{r}
library(enrichR)
setEnrichrSite("Enrichr") # Human genes
# list of all the databases
# get the possible libraries
dbs <- listEnrichrDbs()

# this will list the possible libraries
dbs

# select libraries with cell types
db <- c('CellMarker_Augmented_2021','Azimuth_Cell_Types_2021')

# function for a quick look
checkCelltypes <- function(cluster_num = 0){
  clusterX <- ClusterMarkers %>% filter(cluster == cluster_num & avg_log2FC > 0.25)
  genes <- clusterX$gene
  # the cell type libraries
  # get the results for each library
  clusterX.cell <- enrichr(genes, databases = db)
  # visualize the results
print(plotEnrich(clusterX.cell[[1]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value", title = 'CellMarker_Augmented_2021'))
print(plotEnrich(clusterX.cell[[2]], showTerms = 20, numChar = 40, y = "Count", orderBy = "P.value", title = 'Azimuth_Cell_Types_2021'))

}


```

Plot top markers

```{r}
head(ClusterMarkers)
top5 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=5, wt =avg_log2FC)

DefaultAssay(seu) <- "RNA"
seu <- NormalizeData(seu)
seu <- ScaleData(seu)

top5

top2 <- ClusterMarkers %>% group_by(cluster) %>% top_n(n=2, wt =avg_log2FC)
DoHeatmap(seu, features = top5$gene, size = 3, angle = 90, group.by = "integrated_snn_res.0.3")

DoHeatmap(seu, features = top2$gene, size = 3, angle = 90, group.by = "integrated_snn_res.0.3")

```



Quick look

```{r}
checkCelltypes(cluster_num = 11)

```

Add cell types - fine resolution

```{r}

#library(CelltypeR)
celltypes1 <- c("NPC-stem-ELMO1","NeuronGABA","Progenitor","Epithelial-RG",
                "NPC-div-PAX6-SOX6","Neurons_im1","Neurons_im2",
                "NPC-div-PAX6","NeuronsGlut","RG-stem","NPC-PAX6","Endothelial")

seu <- annotate(seu, annotations = celltypes1, to_label = "integrated_snn_res.0.3",annotation_name = "Celltypes1")
DimPlot(seu, label = TRUE)

# cell types main
celltypesmain <- c("NPC","Neuron","NPC","Epithelial",
                "NPC","Neuron","Neuron",
                "NPC","Neuron","Stem","NPC","Endothelial")

seu <- annotate(seu, annotations = celltypesmain, to_label = "integrated_snn_res.0.3",annotation_name = "CelltypesMain")
DimPlot(seu, label = TRUE)

# some subgroups
celltypes2 <- c("NPC1","Neuron1","Progenitor","Epithelial",
                "NPC2","Neuron2","Neurons3",
                "NPC3","Neurons4","Stem","NPC4","Endothelial")

seu <- annotate(seu, annotations = celltypes2, to_label = "integrated_snn_res.0.3",annotation_name = "Celltypes2")
DimPlot(seu, label = TRUE)


celltypes3 <- c("NPC-div","NeuronGABA","Progenitor","Epithelial",
                "NPC-DA","NeuronDA","Neuron",
                "NPC-div","NeuronDA","Stem","NPC-DA","Endothelial")

seu <- annotate(seu, annotations = celltypes3, to_label = "integrated_snn_res.0.3",annotation_name = "Celltypes3")
DimPlot(seu, label = TRUE)

```

Save this object.



Now get the aggregated data for differential expression

```{r}

Idents(seu) <- "Celltype3s"
sum_counts <- AggregateExpression(seu, assay = "RNA", group.by = c("Celltypes3","Line","DiseaseStatus"))
# this creates a list
sum_counts_df <- as.data.frame(sum_counts$RNA)
head(sum_counts_df)
class(sum_counts_df)
dim(sum_counts_df)
# dataframe with rows as genes and columns as the sample names 
# names include celltype_line_diseasestatus

# example NPC-div_2965_PD

write.csv(sum_counts_df, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Experiment1-mini12/SumCounts_NPC_from48sample.csv")

```
To run DGE we need to make the meta data table

```{r}
# Assuming df is the name of your dataframe
df <- sum_counts_df

sample <- as.data.frame(colnames(df))
colnames(sample) <- "sample"
print(sample$sample)


# Extract values for "Line," and "DiseaseStatus" and "Celltype" using regular expressions
sample_names <- sample$sample
sample_info <- strsplit(sample_names, "_")
sample_info <- do.call(rbind, sample_info)

# Create new columns for "Batch," "Line," and "DiseaseStatus"
sample$Celltype <- sample_info[, 1]
sample$Line <- sample_info[, 2]
sample$DiseaseStatus <- sample_info[, 3]

# Display the updated dataframe
print(sample)
meta.df <- sample

head(meta.df)

```

```{r}
library(DESeq2)

# Create an empty list to store the results for each cell type
list.results <- vector("list", length(Celltypes))

# Loop through each cell type and perform DESeq analysis
for (i in seq_along(Celltypes)) {
  # Subset the expression dataframe by the current cell type
  print(Celltypes[i])
  df_sub <- df.trans[grepl(paste0("^", Celltypes[i], "_"), rownames(df.trans)), ]
  print(dim(df_sub))
  # test one cell group
  #i = "NPC-div"
  df.meta_sub <- df.meta[df.meta$Celltype == Celltypes[i], ]
  print(dim(df.meta_sub))
  # Prepare the DESeq object
  dft <- as.data.frame(t(df_sub)) # Transpose the subset dataframe to get genes as rows and samples as columns
  dfi <- lapply(dft, as.integer)
  dfi <- as.data.frame(dfi)
  rownames(dfi) <- rownames(dft)
  # Create the DESeqDataSet object using the subset dataframe and metadata
  dds <- DESeqDataSetFromMatrix(countData = dfi, colData = df.meta_sub, design = ~DiseaseStatus)
  # Perform DESeq analysis
  dds <- DESeq(dds)
  # Store the DESeq results in the list with the cell type as the list index
  res <- results(dds)
  list2 <- list()
  list2[["dds"]] <- dds
  list2[["results"]] <- res
  
  # Initialize an empty list to store the results for each contrast
  all_results <- list()
  # Loop through each contrast and calculate the results
  for (j in 1:(length(all_contrasts) - 1)) {
    for (k in (j + 1):length(all_contrasts)) {
      contrast_level1 <- all_contrasts[j]
      contrast_level2 <- all_contrasts[k]
      
      # Check if both levels have at least one sample
      if (any(dds$DiseaseStatus %in% c(contrast_level1, contrast_level2))) {
        # Check if contrast levels are different
        if (contrast_level1 != contrast_level2) {
          contrast_name <- paste("DiseaseStatus", contrast_level1, "vs", contrast_level2)
          # Filter rows with complete cases for the current contrast levels
          complete_cases <- complete.cases(dds$DiseaseStatus, dds$DiseaseStatus %in% c(contrast_level1, contrast_level2))
          # Subset the DESeq object and calculate the contrast results
          dds_sub <- dds[complete_cases, ]
          
          # Check if both levels still exist in the subset after removing missing values
          if (contrast_level1 %in% unique(dds_sub$DiseaseStatus) && contrast_level2 %in% unique(dds_sub$DiseaseStatus)) {
            contrast_result <- results(dds_sub, contrast = c("DiseaseStatus", contrast_level1, contrast_level2), name = contrast_name)
            all_results[[contrast_name]] <- contrast_result
          } else {
            message(paste("Skipping", contrast_name, "due to missing contrast levels in DiseaseStatus."))
          }
        } else {
          message(paste("Skipping", contrast_level1, "vs", contrast_level2, "since they are the same level."))
        }
      }
    }
  }
  
  # Add the contrast results to the list
  list2[["contrast_results"]] <- all_results
  
  # Store the list for the current cell type in the appropriate slot
  list.results[[i]] <- list2
}



names(list.results) <- Celltypes



```

Same loop that should add the cell type names

```{r}
# Create an empty list to store the results for each cell type
list.results <- setNames(vector("list", length(Celltypes)), Celltypes)

# Loop through each cell type and perform DESeq analysis
for (i in seq_along(Celltypes)) {
  # Subset the expression dataframe by the current cell type
  print(Celltypes[i])
  df_sub <- df.trans[grepl(paste0("^", Celltypes[i], "_"), rownames(df.trans)), ]
  print(dim(df_sub))
  # test one cell group
  #i = "NPC-div"
  df.meta_sub <- df.meta[df.meta$Celltype == Celltypes[i], ]
  print(dim(df.meta_sub))
  # Prepare the DESeq object
  dft <- as.data.frame(t(df_sub)) # Transpose the subset dataframe to get genes as rows and samples as columns
  dfi <- lapply(dft, as.integer)
  dfi <- as.data.frame(dfi)
  rownames(dfi) <- rownames(dft)
  # Create the DESeqDataSet object using the subset dataframe and metadata
  dds <- DESeqDataSetFromMatrix(countData = dfi, colData = df.meta_sub, design = ~DiseaseStatus)
  # Perform DESeq analysis
  dds <- DESeq(dds)
  # Store the DESeq results in the list with the cell type as the list index
  res <- results(dds)
  list2 <- list()
  list2[["dds"]] <- dds
  list2[["results"]] <- res
  
  # Initialize an empty list to store the results for each contrast
  all_results <- list()
  # Loop through each contrast and calculate the results
  for (j in 1:(length(all_contrasts) - 1)) {
    for (k in (j + 1):length(all_contrasts)) {
      contrast_level1 <- all_contrasts[j]
      contrast_level2 <- all_contrasts[k]
      
      # Check if both levels have at least one sample
      if (any(dds$DiseaseStatus %in% c(contrast_level1, contrast_level2))) {
        # Check if contrast levels are different
        if (contrast_level1 != contrast_level2) {
          contrast_name <- paste("DiseaseStatus", contrast_level1, "vs", contrast_level2)
          # Filter rows with complete cases for the current contrast levels
          complete_cases <- complete.cases(dds$DiseaseStatus, dds$DiseaseStatus %in% c(contrast_level1, contrast_level2))
          # Subset the DESeq object and calculate the contrast results
          dds_sub <- dds[complete_cases, ]
          
          # Check if both levels still exist in the subset after removing missing values
          if (contrast_level1 %in% unique(dds_sub$DiseaseStatus) && contrast_level2 %in% unique(dds_sub$DiseaseStatus)) {
            contrast_result <- results(dds_sub, contrast = c("DiseaseStatus", contrast_level1, contrast_level2), name = contrast_name)
            all_results[[contrast_name]] <- contrast_result
          } else {
            message(paste("Skipping", contrast_name, "due to missing contrast levels in DiseaseStatus."))
          }
        } else {
          message(paste("Skipping", contrast_level1, "vs", contrast_level2, "since they are the same level."))
        }
      }
    }
  }
  
  # Add the contrast results to the list
  list2[["contrast_results"]] <- all_results
  
  # Store the list for the current cell type in the appropriate slot with the cell type name
  list.results[[Celltypes[i]]] <- list2
}


```





```{r}
# Create an empty list to store the results for each cell type and contrast
# Create an empty list to store the results for each cell type
list.results <- list()
# Loop through each cell type and perform DESeq analysis
# Create an empty list to store the results for each cell type
list.results <- vector("list", length(Celltypes))

# Loop through each cell type and perform DESeq analysis
for (i in seq_along(Celltypes)) {
  # ... (your previous code to subset and prepare the DESeq object)

  # Initialize an empty list to store the results for each contrast
  all_results <- list()
  # Loop through each contrast and calculate the results
  for (i in 1:(length(all_contrasts) - 1)) {
    for (j in (i + 1):length(all_contrasts)) {
      contrast_level1 <- all_contrasts[i]
      contrast_level2 <- all_contrasts[j]

      # Check if both levels have at least one sample
      if (any(dds$DiseaseStatus %in% c(contrast_level1, contrast_level2))) {
        # Check if contrast levels are different
        if (contrast_level1 != contrast_level2) {
          contrast_name <- paste("DiseaseStatus", contrast_level1, "vs", contrast_level2)
          # Filter rows with complete cases for the current contrast levels
          complete_cases <- complete.cases(dds$DiseaseStatus, dds$DiseaseStatus %in% c(contrast_level1, contrast_level2))
          # Subset the DESeq object and calculate the contrast results
          dds_sub <- dds[complete_cases, ]

          # Check if both levels still exist in the subset after removing missing values
          if (contrast_level1 %in% unique(dds_sub$DiseaseStatus) && contrast_level2 %in% unique(dds_sub$DiseaseStatus)) {
            contrast_result <- results(dds_sub, contrast = c("DiseaseStatus", contrast_level1, contrast_level2), name = contrast_name)
            all_results[[contrast_name]] <- contrast_result
          } else {
            message(paste("Skipping", contrast_name, "due to missing contrast levels in DiseaseStatus."))
          }
        } else {
          message(paste("Skipping", contrast_level1, "vs", contrast_level2, "since they are the same level."))
        }
      }
    }
  }

  # Store the DESeq results and objects in the list
  list.results[[i]] <- list(
    dds = dds,
    results = all_results
  )
}


```


Summarize the DGEs


```{r}
summarize_contrast <- function(result) {
  num_de_genes <- sum(result$padj <= 0.05, na.rm = TRUE)
  num_downregulated <- sum(result$padj <= 0.05 & result$log2FoldChange < 0, na.rm = TRUE)
  num_upregulated <- sum(result$padj <= 0.05 & result$log2FoldChange > 0, na.rm = TRUE)
  
  return(data.frame(NumGenes = num_de_genes, NumDownregulated = num_downregulated, NumUpregulated = num_upregulated))
}

# Initialize an empty list to store the summary results for each cell type and contrast
summary_results_list <- vector("list", length(Celltypes))
names(summary_results_list) <- Celltypes

# Loop through each cell type and summarize the results for each contrast
for (i in seq_along(Celltypes)) {
  # Get the results for the current cell type
  all_results_celltype <- list.results[[Celltypes[i]]][["contrast_results"]]
  
  # Initialize lists to store the summary results for each contrast
  contrasts <- names(all_results_celltype)
  num_contrasts <- length(contrasts)
  celltype_summary <- data.frame(
    Celltype = rep(Celltypes[i], num_contrasts),
    Contrast = contrasts,
    DGE_total = numeric(num_contrasts),
    DGE_up = numeric(num_contrasts),
    DGE_down = numeric(num_contrasts)
  )
  
  # Loop through each contrast and summarize the results
  for (j in seq_along(contrasts)) {
    contrast_name <- contrasts[j]
    contrast_result <- all_results_celltype[[contrast_name]]
    summary_result <- summarize_contrast(contrast_result)
    
    # Store the summary results for the current contrast
    celltype_summary[j, "DGE_total"] <- summary_result$NumGenes
    celltype_summary[j, "DGE_up"] <- summary_result$NumUpregulated
    celltype_summary[j, "DGE_down"] <- summary_result$NumDownregulated
  }
  
  # Store the summary results for the current cell type
  summary_results_list[[Celltypes[i]]] <- celltype_summary
}

# Combine all the summary results into a single data frame
summary_table <- do.call(rbind, summary_results_list)

# Print the summary table
print(summary_table)
#"/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Experiment1-mini12/SumCounts_NPC_from48sample.csv"
write.csv(summary_table,"/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/DGE_summary_counts_NPCcultures.csv")
saveRDS(list.results, "/Users/rhalenathomas/Documents/Data/scRNAseq/ParseExample/Ex2_48well/DGE_NPCculturesList.RDS")

```


```{r}

res.NPC <- as.data.frame(list.results[["NPC-div"]][["contrast_results"]][["DiseaseStatus HC vs PD"]])

head(res.NPC)

```

```{r, fig.height=3}
library(EnhancedVolcano)
EnhancedVolcano(res.NPC,
    lab = rownames(res.NPC),
    x = 'log2FoldChange',
    y = 'pvalue',
    #ylim = c(0,20),
    #xlim = c(-2,2),
    pCutoff = 0.001,
    FCcutoff = 1,
    pointSize = 5.0,
    labSize = 5, 
    #legendLabSize = 20,
    #subtitleLabSize = 20,
    #legendIconSize = 10,
    ) 
```


